diff --git "a/related_34K/test_related_short_2404.16645v1.json" "b/related_34K/test_related_short_2404.16645v1.json" new file mode 100644--- /dev/null +++ "b/related_34K/test_related_short_2404.16645v1.json" @@ -0,0 +1,1441 @@ +[ + { + "url": "http://arxiv.org/abs/2404.16645v1", + "title": "Tele-FLM Technical Report", + "abstract": "Large language models (LLMs) have showcased profound capabilities in language\nunderstanding and generation, facilitating a wide array of applications.\nHowever, there is a notable paucity of detailed, open-sourced methodologies on\nefficiently scaling LLMs beyond 50 billion parameters with minimum\ntrial-and-error cost and computational resources. In this report, we introduce\nTele-FLM (aka FLM-2), a 52B open-sourced multilingual large language model that\nfeatures a stable, efficient pre-training paradigm and enhanced factual\njudgment capabilities. Tele-FLM demonstrates superior multilingual language\nmodeling abilities, measured by BPB on textual corpus. Besides, in both English\nand Chinese foundation model evaluation, it is comparable to strong\nopen-sourced models that involve larger pre-training FLOPs, such as Llama2-70B\nand DeepSeek-67B. In addition to the model weights, we share the core designs,\nengineering practices, and training details, which we expect to benefit both\nthe academic and industrial communities.", + "authors": "Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Chao Wang, Xinzhang Liu, Zihan Wang, Yu Zhao, Xin Wang, Yuyao Huang, Shuangyong Song, Yongxiang Li, Zheng Zhang, Bo Zhao, Aixin Sun, Yequan Wang, Zhongjiang He, Zhongyuan Wang, Xuelong Li, Tiejun Huang", + "published": "2024-04-25", + "updated": "2024-04-25", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "label": "Original Paper", + "paper_cat": "LLM Fairness", + "gt": "The idea of large foundation models originates from unsupervised pre-training with Transformerbased [64] architectures. Well-known examples of early foundation models include Bert [17], GPT-2 [43], and T5 [45]. GPT-3 [10] increases the model size to 175B and observes decent few-shot and zero-shot reasoning capabilities, which encourages a series of efforts to scale up foundation models [81; 47; 4; 79]. Research on scaling laws [29; 23; 24; 75] sheds light on the predictable trends of model performance when the parameter number increases. On the other hand, other works explore the emergent abilities [68; 67; 48] and their relationships to evaluation metrics and task nature. The Llama series [60; 61; 2] is well-known for its contributions to open-sourced large language models, and is widely regarded as a strong baseline for foundation model evaluation. Falcon [42] explores data processing of publicly available pre-training corpora. Mistral [27] and Gemma [58] release 7B-scaled models that are trained with more data and incorporated with advanced designs. For the Chinese community, Qwen [5], Baichuan [71], Yi [76], and DeepSeek [7] represent efforts in multilingual foundation model pre-training and open-sourcing. FLM-101B [33] studies methodologies for training large foundation models under limited budgets. InstructGPT [41] establishes the paradigm of aligning large foundation models with human preferences. Widely used approaches include supervised fine-tuning (SFT) [66; 70] and Reinforcement Learning from Human Feedback (RLHF) [49], among others [44]. Aligning techniques turn foundation models into dialogue agents, which form the core of AI assistants in commercial use. Closed-source dialogue agents are represented by GPT-4 [40], Claude [6], Grok [1], and Gemini [57]. Open-sourced chat models include Zephyr [62] and ChatGLM [25], among the large number of human-aligned versions of the open foundation models mentioned above.", + "pre_questions": [], + "main_content": "Introduction Large Language Models (LLMs) have been considered a remarkable approach for unsupervised learning, utilizing extensive data to achieve significant advancements. Large models based on decoder-only Transformers [64; 43] have demonstrated strong abilities on language understanding, generation, and in-context learning [10], et al.. Through downstream supervised fine-tuning (SFT) and task-specific alignments (e.g., Reinforcement Learning from Human Feedback, RLHF) [41], LLMs have led to significant progress in the development of dialogue assistant applications with their human-level multi-turn interaction capabilities [40]. Furthermore, LLMs have demonstrated complex cognitive abilities as reflected by code interpretation and completion [37], mathematical problem-solving [35], logical reasoning [69], and agent-like actions [9]. Recently, LLMs have also shown potential to facilitate a unified sequence-to-sequence modeling paradigm for multimodal learning by treating image, video, and audio signals all as token sequences [57; 30]. This positions LLMs as pivotal for progress towards Artificial General Intelligence (AGI) [11]. Inspired by the superior performances of proprietary applications [40; 6], a plethora of open-sourced LLMs has been publicly available for both the English [60; 61; 42; 27; 58] and Chinese [71; 5; 7; 33] communities. The open-sourced models typically vary in size from 7B to 70B parameters, with their performances improving with model sizes and training FLOPs, which is described as scaling laws [29; 23]. Open LLMs can be classified into foundation language models, SFT models, and RLHF models. \u2020Indicates equal contribution. *Corresponding authors. Technical Report. April 26, 2024 (v1) arXiv:2404.16645v1 [cs.CL] 25 Apr 2024 Tele-FLM Technical Report 2 PRE-TRAINING DATA Despite the growing prevalence and impressive evaluation performances, the high computational cost remains the major challenge in LLM development. In this study, we focus on alleviating the excessive computation by establishing a model-producing pipeline that streamlines the hyperparameter searching process, minimizes trial-and-error, and reduces restarts in training. For instance, the Llama technical report [60] assumed the use of around 2,048 A100 GPUs for 5 months, while a single Llama-65B training trial spanned only 21 days, constituting only 14% of the total GPU time. It indicates that open-source endeavors of pre-training LLMs may undergo redundant trial-and-error cycles that may consume enormous computational resources. In contrast, in this work, we reduce the total time cost due to restarts and trial-and-error to negligible levels. We believe that sharing our detailed techniques, engineering practices, and training dynamics [20], especially for LLMs exceeding the 50B scale, could benefit the community as well as contribute to green AI. In this report, we introduce Tele-FLM (aka FLM-2), an open multilingual LLM with 52 billion parameters, which is pre-trained from scratch on a 2.0 trillion token corpus comprising texts from English, Chinese, and various other languages. Tele-FLM inherits and extends the low carbon techniques and fact-enhancing pre-training objectives from the FLM family [33]. The training of Tele-FLM has encountered no instability issue except hardware failures through the completed 2T tokens, and remains ongoing for more data. In addition to the model checkpoints, we release the details of data composition, model architecture, hyperparameter searching, and the full pre-training dynamics. We evaluate Tele-FLM across multiple English and Chinese benchmarks. Regarding English language modeling, Tele-FLM has better Bits-Per-Byte (BPB) than Llama2-70B [61], demonstrating strong compression capabilities. The model also achieves lower BPB than Llama3-70B [2] and Qwen1.572B [5] on Chinese corpora, showcasing its multilingual nature. With fewer English training tokens and smaller models, Tele-FLM matches Llama-65B and is comparable to Llama2-70B in English foundation model evaluation. As for Chinese foundation model evaluation, Tele-FLM matches the overall performance of larger multilingual models trained with a similar amount of data (e.g., DeepSeek-67B [7]). On certain tasks, it surpasses larger models trained with significantly more data (e.g., Qwen1.5-72B). The remainder of this report is structured as follows: Section 2 delves into the specifics of pretraining data processing. Section 3 details our model architecture, tokenizer, infrastructures, training techniques, and hyperparameters. In Section 4, we illustrate the pre-training dynamics and conduct BPB-based evaluation and analysis. Benchmark evaluation in both English and Chinese are provided in Section 5. Section 6 discusses some common issues and lessons learned. Section 7 reviews related literature. We conclude our work and look to the future in Section 8. 2 Pre-training Data Our training dataset comprises a variety of domains, as detailed in Table 1. We build a custom pipeline on spark cluster for massive data processing and apply custom functions to each subset. The pipeline includes text extraction from HTML/WARC, cleaning and paragraph-level deduplication with heuristic rules, model-based quality filtering and document-level deduplication with MinHash [8] algorithm. We obtain 2T tokens after all the procedures, and the distribution ratio between English and Chinese data is roughly 2:1. We incorporate more English data because of its higher quality, especially regarding the WebText domain. Additionally, in line with the methodology of GPT-4, we collected some instruct data and incorporated it into our pre-training data after removing the test sets of common datasets using the strict n-gram-based method. We deliberately avoid \u201ctraining on the test set\u201d or any other benchmark-oriented trick. WebText. CommonCrawl1 is often considered to be a repository containing diverse human experience and rich knowledge (especially long-tail knowledge). However, the high-quality sources in CommonCrawl are primarily concentrated in the English segment, with the Chinese content exhibiting relatively lower information density and quality. We use the latest CommonCrawl dumps from RedPajama [15] and incorporate WudaoCorpora [77] and similar Chinese-specific datasets together to form a large web-text dataset. We apply custom heuristic rules and a FastText [28] classifier to 1https://commoncrawl.org/. 2 Tele-FLM Technical Report 3 PRE-TRAINING DETAILS Table 1: Pre-training data. For each subset of our 2T pre-training tokens, we detail the language, the sampling proportion, the number of epochs completed during training, and the disk size. Domain Language Sampling Prop. Epochs Disk Size WebText en, zh 75.21% 1.0 5.9 TB Code code, zh 9.81% 1.0 528.1 GB Book en, zh 7.17% 0.8 647.6 GB WorldKnowledge multi., en, zh 2.87% 2.5 67.5 GB QA en, zh 2.12% 1.0 159.2 GB AcademicPaper en 0.99% 1.0 54.4 GB Profession-Law zh 1.04% 1.0 84.2 GB Profession-Math math 0.62% 2.0 6.1 GB Profession-Patent zh 0.14% 1.0 10.4 GB Profession-Medical zh 0.02% 1.0 1.2 GB ClassicalChinese zh 0.02% 2.5 0.5 GB filter out low-quality content, cross-deduplicate for each language, and up-sample/down-sample each subset with regard to data quality. The ratio of English to Chinese is approximately 2:1. Code. We incorporate multiple Github-like code datasets and post-process it to filter-out low quality and duplicated content. Simultaneously, we carefully assembled and curated a well-formed markdown dataset comprising Chinese technical articles. Book. We collect books from various sources in both English and Chinese, such as Redpajama [15] and Gutenberg2, among others. We develop a series of cleaning steps to remove redundant formatting, garbled text, formula errors, duplicated paragraphs, and other unwanted content from the books. After interleaved deduplication on document level, we finally obtain a high-quality book dataset. The ratio of English to Chinese is nearly 1:1. WorldKnowledge. To enrich the model\u2019s knowledge base and common sense, we add Wikipedia dumps3 from 2024 period to our training set, covering 22 languages: bg, ca, cs, da, de, en, es, fr, hr, hu, it, ja, nl, pl, pt, ro, ru, sl, sr, sv, uk, zh. We first process these dumps via Online Language Modelling Dataset Pipeline [59] to clean up format; then a meticulous multi-lingual cleaning function is applied to remove reference and subsequent content, which tend to be irrelevant to the main text. QA. We use StackExchange dataset provided by RedPajama-Data [15]. Furthermore, similar Chinese datasets are collected and incorporated into the training after filtering out those QA pairs with low information content. The ratio of English to Chinese in this subset is roughly 1:2. AcademicPaper. We use arxiv dataset collected and processed by RedPajama-Data. This dataset is processed following a Llama-like procedure, which mainly focuses on clearing useless or redundant formats for better language modeling. Profession. To enhance the model\u2019s capacity in various professional fields, we decide to include some specific domains in our dataset, including medical, law, patent, and math. Some subsets are from open-source data, such as Wanjuan-Patent [21] and MathGLM [74]. We post-process each subset independently to address formatting issues, private information disclosure, et al.. ClassicalChinese. In order to improve the model\u2019s understanding of traditional Chinese culture and its capability in classical Chinese, we carefully collect classic Chinese ancient books and poetry. These materials are more credible than those found in web texts; therefore, we assign them a larger weight during sampling. 3 Pre-training Details 3.1 Model Architecture We adapt the architecture of FLM-101B [33] as a backbone with several modifications. FLM-101B follows the standard GPT-style decoder-only transformer architecture [43], with pre-normalization 2https://www.gutenberg.org/. 3https://dumps.wikimedia.org/. 3 Tele-FLM Technical Report 3 PRE-TRAINING DETAILS Table 2: Detailed model architecture. The model configuration of Tele-FLM\u00b5P is a reduced version of Tele-FLM with a smaller hidden size. Models Layer Num Attention Heads Hidden Size FFN Hidden Size Vocab Size Context Length Params Size (M) Tele-FLM 64 64 8,192 21,824 80,000 4,096 52,850 Tele-FLM\u00b5P 64 4 512 1,344 80,000 4,096 283 Table 3: Tokenizer compression ratio. Tokenizer Compression Ratio is defined as the ratio of token length to the original UTF-8 text length. Smaller values indicate better compression. We report the compression ratios of GPT-4, Llama1/2, Llama3, and Tele-FLM on various domains in our training set, as well as the weighted average. Tokenizer Vocab Size Compression Rate English Chinese Classical Chinese Code Multilingual Mathematical Weighted Avg. GPT-4 100k 0.221 0.420 0.478 0.267 0.303 0.508 0.291 Llama1/2 32k 0.262 0.515 0.558 0.367 0.314 0.974 0.356 Llama3 128k 0.220 0.294 0.353 0.267 0.274 0.508 0.251 Tele-FLM 80k 0.248 0.235 0.307 0.363 0.340 0.965 0.261 and adds a LayerNorm to the last layer\u2019s output. Meanwhile, we apply scalar multipliers to: (1) the output of the word embedding layer and (2) the final output hidden states before softmax. We leave these multipliers tunable in pre-training to control the numerical flow. For example, the output multiplier may benefit training by modulating the entropy of the vocabulary distribution. Building on FLM-101B, we further optimize the model structure for Tele-FLM. Specifically, We use RMSNorm [80] for normalization and SwiGLU [50] for the activation function. We roll back to use Rotary Positional Embedding (RoPE) [53] without Extrapolatable Position Embedding (xPos) [55], untie the embedding layer with language modeling head, and disable linear bias in the attention and all MLP modules. One mini version named Tele-FLM\u00b5P is used to search hyper-parameters here. Table 2 details the architecture of both Tele-FLM and Tele-FLM\u00b5P. 3.2 Tokenizer The key to training a text tokenizer is to make a better trade-off between compression ratio and vocabulary size. English-focused tokenizers like GPT-4 or previous Llama series often underperform in compressing Chinese text. In order to guarantee Tele-FLM\u2019s text compression ratio within Chinese while maintaining performance under multilingual setting, we train a tokenizer that aligns closely with the pre-training data distribution. We sample 12 million diverse text samples from our pretraining dataset as the tokenizer\u2019s training dataset, including multilingual texts with a primary focus on Chinese and English, code snippets, classical Chinese literature, and mathematical content. We train the tokenizer with Byte-level BPE (BBPE) algorithm [65]. Table 3 details the tokenizers of Tele-FLM, GPT-4, and the Llama family. The tokenizer of Tele-FLM outperforms GPT-4 and Llama series in both Chinese and Classical Chinese and is comparable with their performances in English, code, and multilingual content. In math, our tokenizer aligns with Llama2 while slightly trailing GPT-4. Overall, Tele-FLM tokenizer showcases a superior compression ratio for Chinese text and satisfactory performance in English. While slightly behind Llama3, Tele-FLM outperforms other approaches on average compression ratio by a large margin. 3.3 Cluster Hardware Tele-FLM is trained on a cluster of 112 A800 SXM4 GPU servers, each with 8 NVLink A800 GPUs and 2TB of RAM. The nodes have heterogeneous CPU architectures: 96 nodes with Intel 8358 (128\u00d7 2.60GHz) CPUs and 16 nodes with AMD 7643 (96\u00d7 2.30GHz) CPUs. All nodes are interconnected via InfiniBand (IB). The training process lasts around two months, including downtime due to unexpected factors. As a comparison of infrastructures, Llama3 [2] is pre-trained on at least 49,152 Nvidia H100 GPUs (in contrast to our 896\u00d7 A800). Meta also claims to have 4 Tele-FLM Technical Report 3 PRE-TRAINING DETAILS the equivalent of 600k H100 GPUs for future computing power4. With this significant gap in total resources, computational efficiency and success rate are critical for average entities. 3.4 Parallelism Tele-FLM utilizes 3D parallel training, combining the prevailing methodologies: data parallelism, tensor parallelism, and pipeline parallelism. Data parallelism [63] is a well-established distributed training method, in which the samples in a batch are partitioned and distributed across multiple devices and processed simultaneously. No inter-device communication is involved in the forward and backward computation, while the gradient is aggregated at the end of each step. Tensor parallelism [51] splits specific neural network tensors across multiple devices and computes via inter-device communication. In Tele-FLM training, tensor parallelism is mainly applied to the attention and feed-forward modules. Excessive use of tensor parallelism may escalate GPU communication overheads and reduce the training speed. To alleviate this, we integrate pipeline parallelism [39] that partitions the model at the layer level. 3D parallelism incorporates these parallel approaches, prioritizing allocation of tensor parallelism groups with higher communication overheads to the same node, thereby maximizing intra-node communication and minimizing inter-node communication. The parallel training setup for Tele-FLM is a mixture of 4 tensor parallel, 2 pipeline parallel, and 112 data parallel. Additionally, we partition inputs to the Transformer\u2019s LayerNorm and Dropout layers along the sequence length dimension with sequence parallelism [31], yielding further GPU computational and memory savings. Furthermore, we utilize Distributed Optimizer module from Megetron-LM5 [46] with optimization. This optimizer further reduces GPU memory consumption by partitioning optimizer states with larger memory footprints across the data parallel dimension. 3.5 Hyperparameter Search Effective hyperparameter tuning may accelerate the loss reduction and ensure convergence, making it crucial for model training. However, the high cost of training large models often renders exhaustive grid searches impractical. Hence, we employ \u00b5P [73] for optimal parameter search. The Tensor Programs theories [72; 36] reveal universal relations in the training dynamics across a series of models, with their widths approaching infinity. For certain hyperparameter classes, this leads to a parameterized mapping for their optimal values between small and large widths. Generally, under \u00b5P transfer, wider models will consistently achieve lower loss than narrower ones when trained on identical data [73]. Consequently, if a narrow model converges, its wider counterparts will always converge. Based on this approach, we set a small model, namely Tele-FLM\u00b5P, for grid search purpose. As demonstrated in Table 2, this small model\u2019s architecture is different from Tele-FLM only in width. With a fixed layer number of 64 and attention head dimension of 128, we reduce the hidden size to 512. This modification results in 4 attention heads and a feed-forward hidden size of 1344. Due to its smaller size, Tele-FLM\u00b5P allows for significantly more experimental runs within fixed time and resource constraints. We search 7 hyperparameters: Learning Rate for vector-like and matrix-like weights, the Minimum Learning Rate at the end of the schedule, the initialization Standard Deviation for vector-like and matrix-like weights, the scaling factor for the embedding layer (namely Input Mult), and the scaling factor for the output hidden state in the final layer (namely Output Mult). For the definitions of vector/matrix-like weights and the \u00b5P transferring formula we apply, please refer to [75] and [73]. We use truncated normal distribution for model initialization. Figure 1 illustrates the loss and gradient norm dynamics of 9 hyperparameter combinations for the grid search, which are selected based on our prior knowledge of model configurations. We choose 4https://www.instagram.com/reel/C2QARHJR1sZ/?hl=en. 5https://github.com/NVIDIA/Megatron-LM. 5 Tele-FLM Technical Report 4 LOSS DYNAMICS AND BPB EVALUATION 0 10000 20000 30000 40000 50000 Steps 2.60 2.65 2.70 2.75 2.80 2.85 2.90 2.95 3.00 Training Loss (a) Loss curves for grid search. 0 10000 20000 30000 40000 50000 Steps 0 2 4 6 8 10 Gradient Norm (b) Gradient norm curves for grid search. Figure 1: Experimental curves of hyperparameter search based on \u00b5P. Table 4: Tele-FLM Training Hyperparameters. Searched Hyperparameters Non-Searched Hyperparameters Learning Rate 1.5e-4 LR Schedule Type cosine Matrix Learning Rate 1.5e-4 LR Schedule (tokens) 2.5T Minimum Learning Rate 1.5e-5 Warmup Step 2,000 Standard Deviation 4e-3 Clip Grad 1.0 Matrix Standard Deviation 4.242e-3 Weight Decay 0.0 Input Mult 1.0 Batch Size (tokens) 5,505,024 Output Mult 3.125e-2 RoPE Theta 10,000 the hyperparameters represented by the red line for final training after assessing the rate of loss decrease, trend stability, and gradient norm stability. Using \u00b5P, we derive the optimal hyperparameter configuration for the final 52B model based on this searched result, which is detailed in Table 4. A more fine-grained search can be conducted with expanded time and budgets. 4 Loss Dynamics and BPB Evaluation 0 250 500 750 1000 1250 1500 1750 2000 Trained T okens (Billions) 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 Training Loss (a) Training loss curve. 0 250 500 750 1000 1250 1500 1750 2000 Trained T okens (Billions) 1.5 1.6 1.7 1.8 1.9 2.0 2.1 2.2 Validation Loss (b) Validation loss curve. 0 250 500 750 1000 1250 1500 1750 2000 Trained T okens (Billions) 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 Gradient Norm (c) Training gradient norm curve. Figure 2: Pre-training curves for Tele-FLM w.r.t. amount of data in billion tokens. We present the curves for training and validation loss and gradient norm on our pre-training data distribution in Figure 2. Figure 2a shows that the training process of Tele-FLM succeeds with a single, stable run without any divergence. This result is predictable with our \u00b5P hyperparameter search mentioned above. Figure 2b indicates that the loss curve generalizes well to validation data without saturation or overfitting. Figure 2c presents the gradient norm. We observe that the reduction in language modeling loss translates well into improvements on downstream tasks. Language modeling is compression [16]. Evaluation metrics related to language perplexity (PPL) are well-known to be closely connected to compression ratio. Moreover, these metrics usually exhibit more stable scaling behavior, making them an authentic foundation of downstream task performance (which is usually measured by more complex and nonlinear metrics [48]). For PPL-related evaluation, we use Bits-Per-Byte (BPB) [38; 18] as our metric, which considers both per-token loss and the 6 Tele-FLM Technical Report 4 LOSS DYNAMICS AND BPB EVALUATION 0.0 0.5 1.0 1.5 2.0 0.50 0.55 0.60 0.65 0.70 0.75 WebT ext (en) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.45 0.50 0.55 0.60 0.65 0.70 AcademicPaper (en) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.60 0.65 0.70 0.75 0.80 Book (en) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.45 0.48 0.50 0.53 0.55 0.58 0.60 0.62 0.65 StackExchange (en) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.45 0.50 0.55 0.60 0.65 0.70 0.75 Wikipedia (multi-language) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.16 0.18 0.20 0.22 0.24 0.26 0.28 0.30 Github (code) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.60 0.80 1.00 1.20 1.40 WebT ext (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.80 1.00 1.20 1.40 1.60 Book (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.60 0.70 0.80 0.90 1.00 1.10 1.20 1.30 WorldKnowledge (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.80 1.00 1.20 1.40 1.60 QA (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 Trained T okens (Trillions) 1.00 1.20 1.40 1.60 1.80 2.00 2.20 ClassicalChinese (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B 0.0 0.5 1.0 1.5 2.0 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00 BPB Loss on Validation Dataset Professional (zh) T ele-FLM Llama-65B Llama2-70B Llama3-8B Llama3-70B Figure 3: BPB curves of Tele-FLM on representative English (en), Chinese (zh), multi-language, and code validation datasets, compared with Llama series. influence of domains and tokenizers. Specifically, on a test corpus in a certain domain, if the total loss is close, a model that tokenizes with a better compression ratio is preferred by the BPB metric. For the English language, we break down the BPB evaluation into 6 different domains, represented by validation datasets from WebText6, Github, Wikipedia, Books, ArXiv, and StackExchange, respectively. We compare with different versions of Llama, including Llama-65B, Llama2-70B, Llama3-8B, and Llama3-70B [2], to analyze how well Tele-FLM fits to compress English data. 6We use text from CommonCrawl and C4, which approximately represent the same source (broad web data). 7 Tele-FLM Technical Report 5 BENCHMARK EVALUATIONS Table 5: BPB of Tele-FLM, Llama family models, and Qwen1.5-72B on English datasets. BPB is computed for 6 dataset categories, with weighted sum results based on Llama [60] and Tele-FLM training data configurations. The best results are in boldface and second-best underlined. Model WebText Github Wikipedia Book ArXiv StackExchange Weighted Sum L-Prop.1 F-Prop.2 Loss Llama-65B 1.650 0.543 1.297 1.791 1.205 1.293 1.572 1.485 Llama2-70B 1.588 0.471 1.198 1.695 1.103 1.220 1.506 1.418 Llama3-70B 1.729 0.597 1.300 1.886 1.042 1.388 1.642 1.556 Qwen1.5-72B 1.996 0.592 1.433 2.107 1.111 1.393 1.878 1.773 Tele-FLM (52B) 1.598 0.314 1.163 1.843 1.153 1.193 1.512 1.411 BPB Llama-65B 0.615 0.286 0.595 0.710 0.590 0.570 0.602 0.574 Llama2-70B 0.592 0.249 0.544 0.672 0.540 0.538 0.576 0.547 Llama3-70B 0.542 0.229 0.513 0.633 0.479 0.497 0.528 0.502 Qwen1.5-72B 0.642 0.234 0.601 0.717 0.521 0.515 0.620 0.586 Tele-FLM (52B) 0.562 0.164 0.570 0.700 0.567 0.531 0.550 0.516 1 L-Prop. (Llama [60] Proportion): 82% : 4.5% : 4.5% : 4.5% : 2.5% : 2.0%. 2 F-Prop. (Tele-FLM Proportion): 75.17% : 13.48% : 3.56% : 5.26% : 1.46% : 1.07%. Table 6: BPB of Tele-FLM, Llama family models and Qwen1.5-72B, on Chinese datasets. BPB is computed for 7 dataset categories, with direct average and weighted sum results based on Tele-FLM training data distributions. Models WebText Code Book World QA Classical Professional Direct Weighted1 Knowledge Chinese Average Sum Loss Llama-65B 1.773 1.236 2.029 1.586 2.076 2.819 1.215 1.819 1.782 Llama2-70B 1.419 1.019 1.542 1.189 1.681 2.233 0.896 1.426 1.414 Llama3-70B 2.152 1.264 2.210 1.722 2.568 2.844 1.109 1.981 2.114 Qwen1.5-72B 2.260 1.405 2.520 1.751 2.888 2.748 0.908 2.069 2.243 Tele-FLM (52B) 1.923 1.096 2.135 1.612 2.530 2.144 0.846 1.755 1.913 BPB Llama-65B 1.325 0.744 1.503 1.161 1.528 2.280 0.919 1.351 1.326 Llama2-70B 1.060 0.614 1.142 0.869 1.237 1.811 0.678 1.059 1.052 Llama3-70B 0.913 0.498 0.943 0.752 1.063 1.458 0.485 0.873 0.897 Qwen1.5-72B 0.759 0.537 0.871 0.663 0.951 1.237 0.329 0.764 0.759 Tele-FLM (52B) 0.643 0.478 0.741 0.619 0.831 0.949 0.290 0.650 0.646 1 Tele-FLM training set Proportion: 76.60% : 1.91% : 11.61% : 1.44% : 4.50% : 0.07% : 3.87%. Figure 3 illustrates the BPB trends w.r.t. to the amount of our pre-training data (in trillion tokens). As training progresses, Tele-FLM surpasses Llama2-70B on WebText, Github, and StackExchange, outperforming Llama-65B and Llama3-8B on almost all datasets, demonstrating strong foundation abilities in English. Numerical results are presented in Table 5. Regarding the weighted sum of BPB, Tele-FLM outperforms Llama-65B, Llama2-70B, Qwen1.5-72B, and Llama3-8B on both Tele-FLM and Llama [60] weighting proportions. Note that Llama3-8B is trained on more than 15T tokens, and these results may indicate that scaling up the model size is still important, despite the rapid growth of the total amount of training data. Similarly to English, we compute BPB across 7 domains with the corresponding Chinese validation data, namely WebText, Code, Book, World Knowledge, QA, Classical Chinese, and Professional. Results are visualized in Figure 3 (with \u201czh\u201d suffix). Specific scores are provided in Table 6. On all these validation corpora, Tele-FLM demonstrates lower BPB than Qwen1.5-72B and the latest Llama3-70B model. Thus, we conclude that our foundation model achieves strong compression performance for Chinese without sacrificing its English language modeling abilities, and vice versa. 5 Benchmark Evaluations 5.1 English: Open LLM, HumanEval, and BBH Benchmarks. We evaluate Tele-FLM on three public and widely-used English benchmarks: Open LLM Leaderboard7, HumanEval [12], and BIG-Bench Hard [52]. 7https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard. 8 Tele-FLM Technical Report 5 BENCHMARK EVALUATIONS \u2022 Open LLM Leaderboard is hosted on Huggingface and includes 6 key tasks to measure a model\u2019s performance on a variety of areas, such as commonsense inference, knowledge capacity, truthfulness, and maths. We report our model\u2019s results with the official evaluation tools (Language Model Evaluation Harness [19]). For the baseline models, we pick the results directly from the Open LLM Leaderboard. \u2022 HumanEval, introduced by OpenAI, tends to evaluate the code generation ability of language models by measuring functional correctness of docstring-prompted output. We choose the pass@5 metric as a trade-off between representing model capability and the evaluation speed. \u2022 Big-Bench Hard is derived from the BIG-Bench benchmark, a diverse evaluation suite that focuses on tasks believed to be beyond the capabilities of current language models. The Big-Bench-Hard, containing 23 challenging tasks, is specifically chosen to represent areas where language models did not surpass average human-rater performance, according to prior evaluations [56]. Table 7: Performance of Tele-FLM and baselines on English benchmarks. Model Average ARC HellaSwag MMLU TruthfulQA WinoGrande GSM8K HumanEval BBH 25-shot 10-shot 5-shot zero-shot 5-shot 5-shot zero-shot 3-shot Llama2-70B 63.39 67.32 87.33 69.83 44.92 83.74 54.06 46.95 52.94 Llama2-13B 50.29 59.39 82.13 55.77 37.38 76.64 22.82 28.66 39.52 Llama-65B 56.98 63.48 86.09 63.93 43.43 82.56 37.23 33.54 45.54 Llama-13B 46.20 56.23 80.93 47.67 39.48 76.24 7.58 23.78 37.72 Tele-FLM (52B) 56.60 59.47 82.25 64.00 43.09 79.40 45.19 34.76 44.60 Results. Table 7 compares Tele-FLM to the Llama series. With 52B parameters and around 1.3T English pre-training tokens, Tele-FLM matches the overall performance of Llama-65B, which is trained on approximately 1.4T tokens. Regarding the nature of different subtasks, Tele-FLM shows advantages over Llama-65B on GSM8K [14] and HumanEval, which focus on reasoning capabilities, but performs slightly worse on some tasks that rely more heavily on knowledge. This disadvantage can potentially be mitigated with more pre-training data consumed. Besides, Tele-FLM achieves > 90% of the performances of Llama2-70B, which is larger in size and trained on a 2T token corpus. 5.2 Chinese: OpenCompass Benchmarks. To measure the Chinese language and knowledge capabilities of our model, we conduct an evaluation using the OpenCompass8 toolkit. Specifically, we choose the following tasks to evaluate the model\u2019s performance in multiple aspects: C-Eval [26] and CMMLU [32] (multisubject knowledge), C3 [54] (reading comprehension), CHID [82] (Chinese culture and language understanding), and CSL [34] (keyword recognition). Results. Table 8 shows evaluation results on Chinese benchmarks. On average, Tele-FLM achieves significantly higher scores than GPT-3.5 and comparable to GPT-4 and DeepSeek-67B [7], reaching 84% of Qwen1.5-72B\u2019s performance [5]. Note that Qwen1.5-72B is larger in size and trained with up to 3T tokens. On CHID and CSL, Tele-FLM shows leading performance among all the models compared. Interestingly, CHID is very specific to Chinese culture, while CSL comes from the scientific domain. This indicates Tele-FLM\u2019s potential to both quickly adapt to a specific language and benefit from general knowledge presented in different languages. 5.3 Evolution of Performance during Training We automatically track the evaluation scores on sampled validation data for 8 of the evaluation benchmarks, as depicted in Figure 4. We observe that for all the tasks, evaluation score improves as pre-training and validation loss/BPB decreases. For knowledge-oriented English benchmarks, including ARC [13], HellaSwag [78], Winogrande [3], and MMLU [22], the performances increase smoothly with more data, which is intuitive regarding the task nature. For reasoning-oriented tasks including GSM8K and BBH, we observe a sharper increase, which indicates these tasks have more complex metrics and could possibly demonstrate emergent abilities. CMMLU is a knowledge-oriented Chinese benchmark. The sharper increase in CMMLU indicates that our Chinese training data is far from saturating, and further improvement can be expected with the ongoing training process. 8https://opencompass.org.cn/home. 9 Tele-FLM Technical Report 6 LESSONS LEARNED Table 8: Performance of Tele-FLM and baselines on Chinese benchmarks. The results of Qwen1.5-72B and our Tele-FLM are locally computed with the OpenCompass toolkit, while other results are picked from OpenCompass leaderboard. Model Average C-Eval CMMLU C3 CHID CSL GPT-4 76.64 69.90 71.00 95.10 82.20 65.00 GPT-3.5 61.86 52.50 53.90 85.60 60.40 56.90 Qwen1.5-72B 80.45 83.72 83.09 81.86 91.09 62.50 Qwen-72B 83.00 83.30 83.60 95.80 91.10 61.20 DeepSeek-67B 73.46 66.90 70.40 77.80 89.10 63.10 Tele-FLM (52B) 71.13 65.48 66.98 66.25 92.57 64.38 0.0 0.5 1.0 1.5 2.0 35 40 45 50 55 60 Acc Norm ARC 0.0 0.5 1.0 1.5 2.0 60 65 70 75 80 Acc Norm HellaSwag 0.0 0.5 1.0 1.5 2.0 10 20 30 40 Acc GSM8K 0.0 0.5 1.0 1.5 2.0 38 39 40 41 42 43 44 Exact Match BBH 0.0 0.5 1.0 1.5 2.0 T okens (T) 38 40 42 44 MC2 TruthfulQA 0.0 0.5 1.0 1.5 2.0 T okens (T) 65 70 75 80 Acc Norm Winogrande 0.0 0.5 1.0 1.5 2.0 T okens (T) 30 35 40 45 50 55 60 65 Acc MMLU 0.0 0.5 1.0 1.5 2.0 T okens (T) 54 56 58 60 62 64 66 68 Acc CMMLU Figure 4: Evolution of performance evaluated by Language Model Evaluation Harness during training. Note that we sampled 20% examples for Hellswag and 30% examples for MMLU considering the time cost. 6 Lessons Learned Lesson on Pre-training Data. We have the following observations in Tele-FLM\u2019s pre-training process. First, as is widely known, both quality and quantity of the data are critical for pre-training; however, when there should be a trade-off between quality and quantity, data quality might be prioritized. For our project, an English-Chinese data ratio of 2:1 works better than 1:1, likely because the average quality of the Chinese Web data we have is relatively low. Second, changing the data distribution midway sometimes leads to changes in gradient norm curves and potential divergence, while maintaining a fixed distribution is more stable. Another advantage of maintaining a fixed data distribution is that it allows for safer early-stop of the \u00b5P experiments. To conclude, the data processing should be as complete as possible before the pre-training starts. Lesson on Hyperparameter Search. We observe that \u00b5P-based methods [73; 75] are effective and efficient in searching for the best hyperparameters and predicting the behaviors of the final large models. Specifically, prior experiences and the open-sourced learning rates are good starting points for hyperparameter search. Nevertheless, initialization standard deviation and output multipliers have more significant influences than commonly known. Lesson on Loss Dynamics. First, the slope of the loss curve typically flattens after 500B tokens. Therefore, training should be restarted promptly if early loss values are unsatisfactory. Second, random loss spikes are common and acceptable if the gradient norm curve looks normal. We observe that our model recovers from all the spikes in the pre-training process, unlike the early open-sourced endeavors [81; 4; 79]. We speculate that modern Llama-like structures, especially those with non-bias designs and truncated normal initialization, combined with effective hyperparameter search, provide decent robustness against loss spikes. Another type of spike corresponds to consistent loss increases, which can be identified early with \u00b5P and avoided before the training begins. 10 Tele-FLM Technical Report REFERENCES Lesson on Gradient Norm. The early gradient norm curves are not strong indicators of training stability. In hyperparameter search, we observe divergence following various gradient curve patterns, yet with higher divergence probabilities associated with continuously increasing gradient trends. In this report, we introduce Tele-FLM, an open multilingual foundation model. With 52B parameters and 2T training tokens, Tele-FLM matches the performance of larger models trained with more data, in both multilingual language modeling capabilities and benchmark evaluations. The pre-training procedure of Tele-FLM features a high success rate and low carbon footprint. We open-source the model weights as well as technical details and training dynamics. We hope this work will catalyze the growth of open-sourced LLM communities and reduce the trial-and-error cycles to train LLMs with more than 50B parameters. Note that although efforts are made to filter out harmful contents in the training data, such kind of outputs could still potentially be elicited from the released model, which does not represent the opinions of the authors or entities involved. For future work, we plan to continue enhancing the capabilities of Tele-FLM to facilitate broader application, as well as to develop efficient training techniques to explore the unmanned deep space of larger-scaled dense models. Acknowledgments This work is supported by the National Science and Technology Major Project (No. 2022ZD0116300) and the National Science Foundation of China (No. 62106249). We would like to thank Boya Wu, Li Du, Quanyue Ma, Hanyu Zhao, Shiyu Wu and Kaipeng Jia for their help on data, Hailong Qian, Jinglong Li, Taojia Liu, Junjie Wang, Yuanlin Cai, Jiahao Guo, Quan Zhao, Xuwei Yang, Hanxiao Qu, Yan Tian, and Kailong Xie for their help on computational resources, and all other colleagues\u2019 strong support for this project." + }, + { + "url": "http://arxiv.org/abs/2305.08322v3", + "title": "C-Eval: A Multi-Level Multi-Discipline Chinese Evaluation Suite for Foundation Models", + "abstract": "New NLP benchmarks are urgently needed to align with the rapid development of\nlarge language models (LLMs). We present C-Eval, the first comprehensive\nChinese evaluation suite designed to assess advanced knowledge and reasoning\nabilities of foundation models in a Chinese context. C-Eval comprises\nmultiple-choice questions across four difficulty levels: middle school, high\nschool, college, and professional. The questions span 52 diverse disciplines,\nranging from humanities to science and engineering. C-Eval is accompanied by\nC-Eval Hard, a subset of very challenging subjects in C-Eval that requires\nadvanced reasoning abilities to solve. We conduct a comprehensive evaluation of\nthe most advanced LLMs on C-Eval, including both English- and Chinese-oriented\nmodels. Results indicate that only GPT-4 could achieve an average accuracy of\nover 60%, suggesting that there is still significant room for improvement for\ncurrent LLMs. We anticipate C-Eval will help analyze important strengths and\nshortcomings of foundation models, and foster their development and growth for\nChinese users.", + "authors": "Yuzhen Huang, Yuzhuo Bai, Zhihao Zhu, Junlei Zhang, Jinghan Zhang, Tangjun Su, Junteng Liu, Chuancheng Lv, Yikai Zhang, Jiayi Lei, Yao Fu, Maosong Sun, Junxian He", + "published": "2023-05-15", + "updated": "2023-11-06", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2209.05034v1", + "title": "CSL: A Large-scale Chinese Scientific Literature Dataset", + "abstract": "Scientific literature serves as a high-quality corpus, supporting a lot of\nNatural Language Processing (NLP) research. However, existing datasets are\ncentered around the English language, which restricts the development of\nChinese scientific NLP. In this work, we present CSL, a large-scale Chinese\nScientific Literature dataset, which contains the titles, abstracts, keywords\nand academic fields of 396k papers. To our knowledge, CSL is the first\nscientific document dataset in Chinese. The CSL can serve as a Chinese corpus.\nAlso, this semi-structured data is a natural annotation that can constitute\nmany supervised NLP tasks. Based on CSL, we present a benchmark to evaluate the\nperformance of models across scientific domain tasks, i.e., summarization,\nkeyword generation and text classification. We analyze the behavior of existing\ntext-to-text models on the evaluation tasks and reveal the challenges for\nChinese scientific NLP tasks, which provides a valuable reference for future\nresearch. Data and code are available at https://github.com/ydli-ai/CSL", + "authors": "Yudong Li, Yuqing Zhang, Zhe Zhao, Linlin Shen, Weijie Liu, Weiquan Mao, Hui Zhang", + "published": "2022-09-12", + "updated": "2022-09-12", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2310.02244v5", + "title": "Tensor Programs VI: Feature Learning in Infinite-Depth Neural Networks", + "abstract": "By classifying infinite-width neural networks and identifying the *optimal*\nlimit, Tensor Programs IV and V demonstrated a universal way, called $\\mu$P,\nfor *widthwise hyperparameter transfer*, i.e., predicting optimal\nhyperparameters of wide neural networks from narrow ones. Here we investigate\nthe analogous classification for *depthwise parametrizations* of deep residual\nnetworks (resnets). We classify depthwise parametrizations of block multiplier\nand learning rate by their infinite-width-then-depth limits. In resnets where\neach block has only one layer, we identify a unique optimal parametrization,\ncalled Depth-$\\mu$P that extends $\\mu$P and show empirically it admits\ndepthwise hyperparameter transfer. We identify *feature diversity* as a crucial\nfactor in deep networks, and Depth-$\\mu$P can be characterized as maximizing\nboth feature learning and feature diversity. Exploiting this, we find that\nabsolute value, among all homogeneous nonlinearities, maximizes feature\ndiversity and indeed empirically leads to significantly better performance.\nHowever, if each block is deeper (such as modern transformers), then we find\nfundamental limitations in all possible infinite-depth limits of such\nparametrizations, which we illustrate both theoretically and empirically on\nsimple networks as well as Megatron transformer trained on Common Crawl.", + "authors": "Greg Yang, Dingli Yu, Chen Zhu, Soufiane Hayou", + "published": "2023-10-03", + "updated": "2023-10-12", + "primary_cat": "cs.NE", + "cats": [ + "cs.NE", + "cond-mat.dis-nn", + "math.PR" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2204.05862v1", + "title": "Training a Helpful and Harmless Assistant with Reinforcement Learning from Human Feedback", + "abstract": "We apply preference modeling and reinforcement learning from human feedback\n(RLHF) to finetune language models to act as helpful and harmless assistants.\nWe find this alignment training improves performance on almost all NLP\nevaluations, and is fully compatible with training for specialized skills such\nas python coding and summarization. We explore an iterated online mode of\ntraining, where preference models and RL policies are updated on a weekly\ncadence with fresh human feedback data, efficiently improving our datasets and\nmodels. Finally, we investigate the robustness of RLHF training, and identify a\nroughly linear relation between the RL reward and the square root of the KL\ndivergence between the policy and its initialization. Alongside our main\nresults, we perform peripheral analyses on calibration, competing objectives,\nand the use of OOD detection, compare our models with human writers, and\nprovide samples from our models using prompts appearing in recent related work.", + "authors": "Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, Jared Kaplan", + "published": "2022-04-12", + "updated": "2022-04-12", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.LG" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2401.02954v1", + "title": "DeepSeek LLM: Scaling Open-Source Language Models with Longtermism", + "abstract": "The rapid development of open-source large language models (LLMs) has been\ntruly remarkable. However, the scaling law described in previous literature\npresents varying conclusions, which casts a dark cloud over scaling LLMs. We\ndelve into the study of scaling laws and present our distinctive findings that\nfacilitate scaling of large scale models in two commonly used open-source\nconfigurations, 7B and 67B. Guided by the scaling laws, we introduce DeepSeek\nLLM, a project dedicated to advancing open-source language models with a\nlong-term perspective. To support the pre-training phase, we have developed a\ndataset that currently consists of 2 trillion tokens and is continuously\nexpanding. We further conduct supervised fine-tuning (SFT) and Direct\nPreference Optimization (DPO) on DeepSeek LLM Base models, resulting in the\ncreation of DeepSeek Chat models. Our evaluation results demonstrate that\nDeepSeek LLM 67B surpasses LLaMA-2 70B on various benchmarks, particularly in\nthe domains of code, mathematics, and reasoning. Furthermore, open-ended\nevaluations reveal that DeepSeek LLM 67B Chat exhibits superior performance\ncompared to GPT-3.5.", + "authors": "DeepSeek-AI, :, Xiao Bi, Deli Chen, Guanting Chen, Shanhuang Chen, Damai Dai, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Zhe Fu, Huazuo Gao, Kaige Gao, Wenjun Gao, Ruiqi Ge, Kang Guan, Daya Guo, Jianzhong Guo, Guangbo Hao, Zhewen Hao, Ying He, Wenjie Hu, Panpan Huang, Erhang Li, Guowei Li, Jiashi Li, Yao Li, Y. K. Li, Wenfeng Liang, Fangyun Lin, A. X. Liu, Bo Liu, Wen Liu, Xiaodong Liu, Xin Liu, Yiyuan Liu, Haoyu Lu, Shanghao Lu, Fuli Luo, Shirong Ma, Xiaotao Nie, Tian Pei, Yishi Piao, Junjie Qiu, Hui Qu, Tongzheng Ren, Zehui Ren, Chong Ruan, Zhangli Sha, Zhihong Shao, Junxiao Song, Xuecheng Su, Jingxiang Sun, Yaofeng Sun, Minghui Tang, Bingxuan Wang, Peiyi Wang, Shiyu Wang, Yaohui Wang, Yongji Wang, Tong Wu, Y. Wu, Xin Xie, Zhenda Xie, Ziwei Xie, Yiliang Xiong, Hanwei Xu, R. X. Xu, Yanhong Xu, Dejian Yang, Yuxiang You, Shuiping Yu, Xingkai Yu, B. Zhang, Haowei Zhang, Lecong Zhang, Liyue Zhang, Mingchuan Zhang, Minghua Zhang, Wentao Zhang, Yichao Zhang, Chenggang Zhao, Yao Zhao, Shangyan Zhou, Shunfeng Zhou, Qihao Zhu, Yuheng Zou", + "published": "2024-01-05", + "updated": "2024-01-05", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.LG" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/1910.02054v3", + "title": "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models", + "abstract": "Large deep learning models offer significant accuracy gains, but training\nbillions to trillions of parameters is challenging. Existing solutions such as\ndata and model parallelisms exhibit fundamental limitations to fit these models\ninto limited device memory, while obtaining computation, communication and\ndevelopment efficiency. We develop a novel solution, Zero Redundancy Optimizer\n(ZeRO), to optimize memory, vastly improving training speed while increasing\nthe model size that can be efficiently trained. ZeRO eliminates memory\nredundancies in data- and model-parallel training while retaining low\ncommunication volume and high computational granularity, allowing us to scale\nthe model size proportional to the number of devices with sustained high\nefficiency. Our analysis on memory requirements and communication volume\ndemonstrates: ZeRO has the potential to scale beyond 1 Trillion parameters\nusing today's hardware.\n We implement and evaluate ZeRO: it trains large models of over 100B parameter\nwith super-linear speedup on 400 GPUs, achieving throughput of 15 Petaflops.\nThis represents an 8x increase in model size and 10x increase in achievable\nperformance over state-of-the-art. In terms of usability, ZeRO can train large\nmodels of up to 13B parameters (e.g., larger than Megatron GPT 8.3B and T5 11B)\nwithout requiring model parallelism which is harder for scientists to apply.\nLast but not the least, researchers have used the system breakthroughs of ZeRO\nto create the world's largest language model (Turing-NLG, 17B parameters) with\nrecord breaking accuracy.", + "authors": "Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He", + "published": "2019-10-04", + "updated": "2020-05-13", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.DC", + "stat.ML" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2101.00027v1", + "title": "The Pile: An 800GB Dataset of Diverse Text for Language Modeling", + "abstract": "Recent work has demonstrated that increased training dataset diversity\nimproves general cross-domain knowledge and downstream generalization\ncapability for large-scale language models. With this in mind, we present\n\\textit{the Pile}: an 825 GiB English text corpus targeted at training\nlarge-scale language models. The Pile is constructed from 22 diverse\nhigh-quality subsets -- both existing and newly constructed -- many of which\nderive from academic or professional sources. Our evaluation of the untuned\nperformance of GPT-2 and GPT-3 on the Pile shows that these models struggle on\nmany of its components, such as academic writing. Conversely, models trained on\nthe Pile improve significantly over both Raw CC and CC-100 on all components of\nthe Pile, while improving performance on downstream evaluations. Through an\nin-depth exploratory analysis, we document potentially concerning aspects of\nthe data for prospective users. We make publicly available the code used in its\nconstruction.", + "authors": "Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, Connor Leahy", + "published": "2020-12-31", + "updated": "2020-12-31", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2403.08295v4", + "title": "Gemma: Open Models Based on Gemini Research and Technology", + "abstract": "This work introduces Gemma, a family of lightweight, state-of-the art open\nmodels built from the research and technology used to create Gemini models.\nGemma models demonstrate strong performance across academic benchmarks for\nlanguage understanding, reasoning, and safety. We release two sizes of models\n(2 billion and 7 billion parameters), and provide both pretrained and\nfine-tuned checkpoints. Gemma outperforms similarly sized open models on 11 out\nof 18 text-based tasks, and we present comprehensive evaluations of safety and\nresponsibility aspects of the models, alongside a detailed description of model\ndevelopment. We believe the responsible release of LLMs is critical for\nimproving the safety of frontier models, and for enabling the next wave of LLM\ninnovations.", + "authors": "Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivi\u00e8re, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, L\u00e9onard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Am\u00e9lie H\u00e9liou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Cl\u00e9ment Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, Justin Mao-Jones, Katherine Lee, Kathy Yu, Katie Millican, Lars Lowe Sjoesund, Lisa Lee, Lucas Dixon, Machel Reid, Maciej Miku\u0142a, Mateo Wirth, Michael Sharman, Nikolai Chinaev, Nithum Thain, Olivier Bachem, Oscar Chang, Oscar Wahltinez, Paige Bailey, Paul Michel, Petko Yotov, Rahma Chaabouni, Ramona Comanescu, Reena Jana, Rohan Anil, Ross McIlroy, Ruibo Liu, Ryan Mullins, Samuel L Smith, Sebastian Borgeaud, Sertan Girgin, Sholto Douglas, Shree Pandya, Siamak Shakeri, Soham De, Ted Klimenko, Tom Hennigan, Vlad Feinberg, Wojciech Stokowiec, Yu-hui Chen, Zafarali Ahmed, Zhitao Gong, Tris Warkentin, Ludovic Peran, Minh Giang, Cl\u00e9ment Farabet, Oriol Vinyals, Jeff Dean, Koray Kavukcuoglu, Demis Hassabis, Zoubin Ghahramani, Douglas Eck, Joelle Barral, Fernando Pereira, Eli Collins, Armand Joulin, Noah Fiedel, Evan Senter, Alek Andreev, Kathleen Kenealy", + "published": "2024-03-13", + "updated": "2024-04-16", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/1909.03341v2", + "title": "Neural Machine Translation with Byte-Level Subwords", + "abstract": "Almost all existing machine translation models are built on top of\ncharacter-based vocabularies: characters, subwords or words. Rare characters\nfrom noisy text or character-rich languages such as Japanese and Chinese\nhowever can unnecessarily take up vocabulary slots and limit its compactness.\nRepresenting text at the level of bytes and using the 256 byte set as\nvocabulary is a potential solution to this issue. High computational cost has\nhowever prevented it from being widely deployed or used in practice. In this\npaper, we investigate byte-level subwords, specifically byte-level BPE (BBPE),\nwhich is compacter than character vocabulary and has no out-of-vocabulary\ntokens, but is more efficient than using pure bytes only is. We claim that\ncontextualizing BBPE embeddings is necessary, which can be implemented by a\nconvolutional or recurrent layer. Our experiments show that BBPE has comparable\nperformance to BPE while its size is only 1/8 of that for BPE. In the\nmultilingual setting, BBPE maximizes vocabulary sharing across many languages\nand achieves better translation quality. Moreover, we show that BBPE enables\ntransferring models between languages with non-overlapping character sets.", + "authors": "Changhan Wang, Kyunghyun Cho, Jiatao Gu", + "published": "2019-09-07", + "updated": "2019-12-05", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2203.02155v1", + "title": "Training language models to follow instructions with human feedback", + "abstract": "Making language models bigger does not inherently make them better at\nfollowing a user's intent. For example, large language models can generate\noutputs that are untruthful, toxic, or simply not helpful to the user. In other\nwords, these models are not aligned with their users. In this paper, we show an\navenue for aligning language models with user intent on a wide range of tasks\nby fine-tuning with human feedback. Starting with a set of labeler-written\nprompts and prompts submitted through the OpenAI API, we collect a dataset of\nlabeler demonstrations of the desired model behavior, which we use to fine-tune\nGPT-3 using supervised learning. We then collect a dataset of rankings of model\noutputs, which we use to further fine-tune this supervised model using\nreinforcement learning from human feedback. We call the resulting models\nInstructGPT. In human evaluations on our prompt distribution, outputs from the\n1.3B parameter InstructGPT model are preferred to outputs from the 175B GPT-3,\ndespite having 100x fewer parameters. Moreover, InstructGPT models show\nimprovements in truthfulness and reductions in toxic output generation while\nhaving minimal performance regressions on public NLP datasets. Even though\nInstructGPT still makes simple mistakes, our results show that fine-tuning with\nhuman feedback is a promising direction for aligning language models with human\nintent.", + "authors": "Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, Ryan Lowe", + "published": "2022-03-04", + "updated": "2022-03-04", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.LG" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2306.01116v1", + "title": "The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Only", + "abstract": "Large language models are commonly trained on a mixture of filtered web data\nand curated high-quality corpora, such as social media conversations, books, or\ntechnical papers. This curation process is believed to be necessary to produce\nperformant models with broad zero-shot generalization abilities. However, as\nlarger models requiring pretraining on trillions of tokens are considered, it\nis unclear how scalable is curation and whether we will run out of unique\nhigh-quality data soon. At variance with previous beliefs, we show that\nproperly filtered and deduplicated web data alone can lead to powerful models;\neven significantly outperforming models from the state-of-the-art trained on\nThe Pile. Despite extensive filtering, the high-quality data we extract from\nthe web is still plentiful, and we are able to obtain five trillion tokens from\nCommonCrawl. We publicly release an extract of 600 billion tokens from our\nRefinedWeb dataset, and 1.3/7.5B parameters language models trained on it.", + "authors": "Guilherme Penedo, Quentin Malartic, Daniel Hesslow, Ruxandra Cojocaru, Alessandro Cappelli, Hamza Alobeidli, Baptiste Pannier, Ebtesam Almazrouei, Julien Launay", + "published": "2023-06-01", + "updated": "2023-06-01", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2305.18290v2", + "title": "Direct Preference Optimization: Your Language Model is Secretly a Reward Model", + "abstract": "While large-scale unsupervised language models (LMs) learn broad world\nknowledge and some reasoning skills, achieving precise control of their\nbehavior is difficult due to the completely unsupervised nature of their\ntraining. Existing methods for gaining such steerability collect human labels\nof the relative quality of model generations and fine-tune the unsupervised LM\nto align with these preferences, often with reinforcement learning from human\nfeedback (RLHF). However, RLHF is a complex and often unstable procedure, first\nfitting a reward model that reflects the human preferences, and then\nfine-tuning the large unsupervised LM using reinforcement learning to maximize\nthis estimated reward without drifting too far from the original model. In this\npaper we introduce a new parameterization of the reward model in RLHF that\nenables extraction of the corresponding optimal policy in closed form, allowing\nus to solve the standard RLHF problem with only a simple classification loss.\nThe resulting algorithm, which we call Direct Preference Optimization (DPO), is\nstable, performant, and computationally lightweight, eliminating the need for\nsampling from the LM during fine-tuning or performing significant\nhyperparameter tuning. Our experiments show that DPO can fine-tune LMs to align\nwith human preferences as well as or better than existing methods. Notably,\nfine-tuning with DPO exceeds PPO-based RLHF in ability to control sentiment of\ngenerations, and matches or improves response quality in summarization and\nsingle-turn dialogue while being substantially simpler to implement and train.", + "authors": "Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn", + "published": "2023-05-29", + "updated": "2023-12-13", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.AI", + "cs.CL" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2303.12712v5", + "title": "Sparks of Artificial General Intelligence: Early experiments with GPT-4", + "abstract": "Artificial intelligence (AI) researchers have been developing and refining\nlarge language models (LLMs) that exhibit remarkable capabilities across a\nvariety of domains and tasks, challenging our understanding of learning and\ncognition. The latest model developed by OpenAI, GPT-4, was trained using an\nunprecedented scale of compute and data. In this paper, we report on our\ninvestigation of an early version of GPT-4, when it was still in active\ndevelopment by OpenAI. We contend that (this early version of) GPT-4 is part of\na new cohort of LLMs (along with ChatGPT and Google's PaLM for example) that\nexhibit more general intelligence than previous AI models. We discuss the\nrising capabilities and implications of these models. We demonstrate that,\nbeyond its mastery of language, GPT-4 can solve novel and difficult tasks that\nspan mathematics, coding, vision, medicine, law, psychology and more, without\nneeding any special prompting. Moreover, in all of these tasks, GPT-4's\nperformance is strikingly close to human-level performance, and often vastly\nsurpasses prior models such as ChatGPT. Given the breadth and depth of GPT-4's\ncapabilities, we believe that it could reasonably be viewed as an early (yet\nstill incomplete) version of an artificial general intelligence (AGI) system.\nIn our exploration of GPT-4, we put special emphasis on discovering its\nlimitations, and we discuss the challenges ahead for advancing towards deeper\nand more comprehensive versions of AGI, including the possible need for\npursuing a new paradigm that moves beyond next-word prediction. We conclude\nwith reflections on societal influences of the recent technological leap and\nfuture research directions.", + "authors": "S\u00e9bastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, Harsha Nori, Hamid Palangi, Marco Tulio Ribeiro, Yi Zhang", + "published": "2023-03-22", + "updated": "2023-04-13", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2002.05202v1", + "title": "GLU Variants Improve Transformer", + "abstract": "Gated Linear Units (arXiv:1612.08083) consist of the component-wise product\nof two linear projections, one of which is first passed through a sigmoid\nfunction. Variations on GLU are possible, using different nonlinear (or even\nlinear) functions in place of sigmoid. We test these variants in the\nfeed-forward sublayers of the Transformer (arXiv:1706.03762)\nsequence-to-sequence model, and find that some of them yield quality\nimprovements over the typically-used ReLU or GELU activations.", + "authors": "Noam Shazeer", + "published": "2020-02-12", + "updated": "2020-02-12", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.NE", + "stat.ML" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/1910.10683v4", + "title": "Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer", + "abstract": "Transfer learning, where a model is first pre-trained on a data-rich task\nbefore being fine-tuned on a downstream task, has emerged as a powerful\ntechnique in natural language processing (NLP). The effectiveness of transfer\nlearning has given rise to a diversity of approaches, methodology, and\npractice. In this paper, we explore the landscape of transfer learning\ntechniques for NLP by introducing a unified framework that converts all\ntext-based language problems into a text-to-text format. Our systematic study\ncompares pre-training objectives, architectures, unlabeled data sets, transfer\napproaches, and other factors on dozens of language understanding tasks. By\ncombining the insights from our exploration with scale and our new ``Colossal\nClean Crawled Corpus'', we achieve state-of-the-art results on many benchmarks\ncovering summarization, question answering, text classification, and more. To\nfacilitate future work on transfer learning for NLP, we release our data set,\npre-trained models, and code.", + "authors": "Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, Peter J. Liu", + "published": "2019-10-23", + "updated": "2023-09-19", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.CL", + "stat.ML" + ], + "label": "Related Work" + }, + { + "url": "http://arxiv.org/abs/2403.15491v1", + "title": "Open Source Conversational LLMs do not know most Spanish words", + "abstract": "The growing interest in Large Language Models (LLMs) and in particular in\nconversational models with which users can interact has led to the development\nof a large number of open-source chat LLMs. These models are evaluated on a\nwide range of benchmarks to assess their capabilities in answering questions or\nsolving problems on almost any possible topic or to test their ability to\nreason or interpret texts. Instead, the evaluation of the knowledge that these\nmodels have of the languages has received much less attention. For example, the\nwords that they can recognize and use in different languages. In this paper, we\nevaluate the knowledge that open-source chat LLMs have of Spanish words by\ntesting a sample of words in a reference dictionary. The results show that\nopen-source chat LLMs produce incorrect meanings for an important fraction of\nthe words and are not able to use most of the words correctly to write\nsentences with context. These results show how Spanish is left behind in the\nopen-source LLM race and highlight the need to push for linguistic fairness in\nconversational LLMs ensuring that they provide similar performance across\nlanguages.", + "authors": "Javier Conde, Miguel Gonz\u00e1lez, Nina Melero, Raquel Ferrando, Gonzalo Mart\u00ednez, Elena Merino-G\u00f3mez, Jos\u00e9 Alberto Hern\u00e1ndez, Pedro Reviriego", + "published": "2024-03-21", + "updated": "2024-03-21", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.18333v3", + "title": "She had Cobalt Blue Eyes: Prompt Testing to Create Aligned and Sustainable Language Models", + "abstract": "As the use of large language models (LLMs) increases within society, as does\nthe risk of their misuse. Appropriate safeguards must be in place to ensure LLM\noutputs uphold the ethical standards of society, highlighting the positive role\nthat artificial intelligence technologies can have. Recent events indicate\nethical concerns around conventionally trained LLMs, leading to overall unsafe\nuser experiences. This motivates our research question: how do we ensure LLM\nalignment? In this work, we introduce a test suite of unique prompts to foster\nthe development of aligned LLMs that are fair, safe, and robust. We show that\nprompting LLMs at every step of the development pipeline, including data\ncuration, pre-training, and fine-tuning, will result in an overall more\nresponsible model. Our test suite evaluates outputs from four state-of-the-art\nlanguage models: GPT-3.5, GPT-4, OPT, and LLaMA-2. The assessment presented in\nthis paper highlights a gap between societal alignment and the capabilities of\ncurrent LLMs. Additionally, implementing a test suite such as ours lowers the\nenvironmental overhead of making models safe and fair.", + "authors": "Veronica Chatrath, Oluwanifemi Bamgbose, Shaina Raza", + "published": "2023-10-20", + "updated": "2023-12-15", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.14769v3", + "title": "Large Language Model (LLM) Bias Index -- LLMBI", + "abstract": "The Large Language Model Bias Index (LLMBI) is a pioneering approach designed\nto quantify and address biases inherent in large language models (LLMs), such\nas GPT-4. We recognise the increasing prevalence and impact of LLMs across\ndiverse sectors. This research introduces a novel metric, LLMBI, to\nsystematically measure and mitigate biases potentially skewing model responses.\nWe formulated LLMBI using a composite scoring system incorporating multiple\ndimensions of bias, including but not limited to age, gender, and racial\nbiases. To operationalise this metric, we engaged in a multi-step process\ninvolving collecting and annotating LLM responses, applying sophisticated\nNatural Language Processing (NLP) techniques for bias detection, and computing\nthe LLMBI score through a specially crafted mathematical formula. The formula\nintegrates weighted averages of various bias dimensions, a penalty for dataset\ndiversity deficiencies, and a correction for sentiment biases. Our empirical\nanalysis, conducted using responses from OpenAI's API, employs advanced\nsentiment analysis as a representative method for bias detection. The research\nreveals LLMs, whilst demonstrating impressive capabilities in text generation,\nexhibit varying degrees of bias across different dimensions. LLMBI provides a\nquantifiable measure to compare biases across models and over time, offering a\nvital tool for systems engineers, researchers and regulators in enhancing the\nfairness and reliability of LLMs. It highlights the potential of LLMs in\nmimicking unbiased human-like responses. Additionally, it underscores the\nnecessity of continuously monitoring and recalibrating such models to align\nwith evolving societal norms and ethical standards.", + "authors": "Abiodun Finbarrs Oketunji, Muhammad Anas, Deepthi Saina", + "published": "2023-12-22", + "updated": "2023-12-29", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY", + "cs.LG", + "I.2.7" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.13840v1", + "title": "Whose Side Are You On? Investigating the Political Stance of Large Language Models", + "abstract": "Large Language Models (LLMs) have gained significant popularity for their\napplication in various everyday tasks such as text generation, summarization,\nand information retrieval. As the widespread adoption of LLMs continues to\nsurge, it becomes increasingly crucial to ensure that these models yield\nresponses that are politically impartial, with the aim of preventing\ninformation bubbles, upholding fairness in representation, and mitigating\nconfirmation bias. In this paper, we propose a quantitative framework and\npipeline designed to systematically investigate the political orientation of\nLLMs. Our investigation delves into the political alignment of LLMs across a\nspectrum of eight polarizing topics, spanning from abortion to LGBTQ issues.\nAcross topics, the results indicate that LLMs exhibit a tendency to provide\nresponses that closely align with liberal or left-leaning perspectives rather\nthan conservative or right-leaning ones when user queries include details\npertaining to occupation, race, or political affiliation. The findings\npresented in this study not only reaffirm earlier observations regarding the\nleft-leaning characteristics of LLMs but also surface particular attributes,\nsuch as occupation, that are particularly susceptible to such inclinations even\nwhen directly steered towards conservatism. As a recommendation to avoid these\nmodels providing politicised responses, users should be mindful when crafting\nqueries, and exercise caution in selecting neutral prompt language.", + "authors": "Pagnarasmey Pit, Xingjun Ma, Mike Conway, Qingyu Chen, James Bailey, Henry Pit, Putrasmey Keo, Watey Diep, Yu-Gang Jiang", + "published": "2024-03-15", + "updated": "2024-03-15", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.SI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.15198v2", + "title": "Do LLM Agents Exhibit Social Behavior?", + "abstract": "The advances of Large Language Models (LLMs) are expanding their utility in\nboth academic research and practical applications. Recent social science\nresearch has explored the use of these ``black-box'' LLM agents for simulating\ncomplex social systems and potentially substituting human subjects in\nexperiments. Our study delves into this emerging domain, investigating the\nextent to which LLMs exhibit key social interaction principles, such as social\nlearning, social preference, and cooperative behavior (indirect reciprocity),\nin their interactions with humans and other agents. We develop a framework for\nour study, wherein classical laboratory experiments involving human subjects\nare adapted to use LLM agents. This approach involves step-by-step reasoning\nthat mirrors human cognitive processes and zero-shot learning to assess the\ninnate preferences of LLMs. Our analysis of LLM agents' behavior includes both\nthe primary effects and an in-depth examination of the underlying mechanisms.\nFocusing on GPT-4, our analyses suggest that LLM agents appear to exhibit a\nrange of human-like social behaviors such as distributional and reciprocity\npreferences, responsiveness to group identity cues, engagement in indirect\nreciprocity, and social learning capabilities. However, our analysis also\nreveals notable differences: LLMs demonstrate a pronounced fairness preference,\nweaker positive reciprocity, and a more calculating approach in social learning\ncompared to humans. These insights indicate that while LLMs hold great promise\nfor applications in social science research, such as in laboratory experiments\nand agent-based modeling, the subtle behavioral differences between LLM agents\nand humans warrant further investigation. Careful examination and development\nof protocols in evaluating the social behaviors of LLMs are necessary before\ndirectly applying these models to emulate human behavior.", + "authors": "Yan Leng, Yuan Yuan", + "published": "2023-12-23", + "updated": "2024-02-22", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.SI", + "econ.GN", + "q-fin.EC" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.09447v2", + "title": "How Trustworthy are Open-Source LLMs? An Assessment under Malicious Demonstrations Shows their Vulnerabilities", + "abstract": "The rapid progress in open-source Large Language Models (LLMs) is\nsignificantly driving AI development forward. However, there is still a limited\nunderstanding of their trustworthiness. Deploying these models at scale without\nsufficient trustworthiness can pose significant risks, highlighting the need to\nuncover these issues promptly. In this work, we conduct an adversarial\nassessment of open-source LLMs on trustworthiness, scrutinizing them across\neight different aspects including toxicity, stereotypes, ethics, hallucination,\nfairness, sycophancy, privacy, and robustness against adversarial\ndemonstrations. We propose advCoU, an extended Chain of Utterances-based (CoU)\nprompting strategy by incorporating carefully crafted malicious demonstrations\nfor trustworthiness attack. Our extensive experiments encompass recent and\nrepresentative series of open-source LLMs, including Vicuna, MPT, Falcon,\nMistral, and Llama 2. The empirical outcomes underscore the efficacy of our\nattack strategy across diverse aspects. More interestingly, our result analysis\nreveals that models with superior performance in general NLP tasks do not\nalways have greater trustworthiness; in fact, larger models can be more\nvulnerable to attacks. Additionally, models that have undergone instruction\ntuning, focusing on instruction following, tend to be more susceptible,\nalthough fine-tuning LLMs for safety alignment proves effective in mitigating\nadversarial trustworthiness attacks.", + "authors": "Lingbo Mo, Boshi Wang, Muhao Chen, Huan Sun", + "published": "2023-11-15", + "updated": "2024-04-02", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.00306v1", + "title": "Probing Explicit and Implicit Gender Bias through LLM Conditional Text Generation", + "abstract": "Large Language Models (LLMs) can generate biased and toxic responses. Yet\nmost prior work on LLM gender bias evaluation requires predefined\ngender-related phrases or gender stereotypes, which are challenging to be\ncomprehensively collected and are limited to explicit bias evaluation. In\naddition, we believe that instances devoid of gender-related language or\nexplicit stereotypes in inputs can still induce gender bias in LLMs. Thus, in\nthis work, we propose a conditional text generation mechanism without the need\nfor predefined gender phrases and stereotypes. This approach employs three\ntypes of inputs generated through three distinct strategies to probe LLMs,\naiming to show evidence of explicit and implicit gender biases in LLMs. We also\nutilize explicit and implicit evaluation metrics to evaluate gender bias in\nLLMs under different strategies. Our experiments demonstrate that an increased\nmodel size does not consistently lead to enhanced fairness and all tested LLMs\nexhibit explicit and/or implicit gender bias, even when explicit gender\nstereotypes are absent in the inputs.", + "authors": "Xiangjue Dong, Yibo Wang, Philip S. Yu, James Caverlee", + "published": "2023-11-01", + "updated": "2023-11-01", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.13862v2", + "title": "A Trip Towards Fairness: Bias and De-Biasing in Large Language Models", + "abstract": "Cheap-to-Build Very Large-Language Models (CtB-LLMs) with affordable training\nare emerging as the next big revolution in natural language processing and\nunderstanding. These CtB-LLMs are democratizing access to trainable Very\nLarge-Language Models (VLLMs) and, thus, may represent the building blocks of\nmany NLP systems solving downstream tasks. Hence, a little or a large bias in\nCtB-LLMs may cause huge harm. In this paper, we performed a large investigation\nof the bias of three families of CtB-LLMs, and we showed that debiasing\ntechniques are effective and usable. Indeed, according to current tests, the\nLLaMA and the OPT families have an important bias in gender, race, religion,\nand profession. In contrast to the analysis for other LLMs, we discovered that\nbias depends not on the number of parameters but on the perplexity. Finally,\nthe debiasing of OPT using LoRA reduces bias up to 4.12 points in the\nnormalized stereotype score.", + "authors": "Leonardo Ranaldi, Elena Sofia Ruzzetti, Davide Venditti, Dario Onorati, Fabio Massimo Zanzotto", + "published": "2023-05-23", + "updated": "2023-08-29", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2308.11483v1", + "title": "Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions", + "abstract": "Large Language Models (LLMs) have demonstrated remarkable capabilities in\nvarious NLP tasks. However, previous works have shown these models are\nsensitive towards prompt wording, and few-shot demonstrations and their order,\nposing challenges to fair assessment of these models. As these models become\nmore powerful, it becomes imperative to understand and address these\nlimitations. In this paper, we focus on LLMs robustness on the task of\nmultiple-choice questions -- commonly adopted task to study reasoning and\nfact-retrieving capability of LLMs. Investigating the sensitivity of LLMs\ntowards the order of options in multiple-choice questions, we demonstrate a\nconsiderable performance gap of approximately 13% to 75% in LLMs on different\nbenchmarks, when answer options are reordered, even when using demonstrations\nin a few-shot setting. Through a detailed analysis, we conjecture that this\nsensitivity arises when LLMs are uncertain about the prediction between the\ntop-2/3 choices, and specific options placements may favor certain prediction\nbetween those top choices depending on the question caused by positional bias.\nWe also identify patterns in top-2 choices that amplify or mitigate the model's\nbias toward option placement. We found that for amplifying bias, the optimal\nstrategy involves positioning the top two choices as the first and last\noptions. Conversely, to mitigate bias, we recommend placing these choices among\nthe adjacent options. To validate our conjecture, we conduct various\nexperiments and adopt two approaches to calibrate LLMs' predictions, leading to\nup to 8 percentage points improvement across different models and benchmarks.", + "authors": "Pouya Pezeshkpour, Estevam Hruschka", + "published": "2023-08-22", + "updated": "2023-08-22", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.14804v1", + "title": "Use large language models to promote equity", + "abstract": "Advances in large language models (LLMs) have driven an explosion of interest\nabout their societal impacts. Much of the discourse around how they will impact\nsocial equity has been cautionary or negative, focusing on questions like \"how\nmight LLMs be biased and how would we mitigate those biases?\" This is a vital\ndiscussion: the ways in which AI generally, and LLMs specifically, can entrench\nbiases have been well-documented. But equally vital, and much less discussed,\nis the more opportunity-focused counterpoint: \"what promising applications do\nLLMs enable that could promote equity?\" If LLMs are to enable a more equitable\nworld, it is not enough just to play defense against their biases and failure\nmodes. We must also go on offense, applying them positively to equity-enhancing\nuse cases to increase opportunities for underserved groups and reduce societal\ndiscrimination. There are many choices which determine the impact of AI, and a\nfundamental choice very early in the pipeline is the problems we choose to\napply it to. If we focus only later in the pipeline -- making LLMs marginally\nmore fair as they facilitate use cases which intrinsically entrench power -- we\nwill miss an important opportunity to guide them to equitable impacts. Here, we\nhighlight the emerging potential of LLMs to promote equity by presenting four\nnewly possible, promising research directions, while keeping risks and\ncautionary points in clear view.", + "authors": "Emma Pierson, Divya Shanmugam, Rajiv Movva, Jon Kleinberg, Monica Agrawal, Mark Dredze, Kadija Ferryman, Judy Wawira Gichoya, Dan Jurafsky, Pang Wei Koh, Karen Levy, Sendhil Mullainathan, Ziad Obermeyer, Harini Suresh, Keyon Vafa", + "published": "2023-12-22", + "updated": "2023-12-22", + "primary_cat": "cs.CY", + "cats": [ + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.06899v4", + "title": "Flames: Benchmarking Value Alignment of LLMs in Chinese", + "abstract": "The widespread adoption of large language models (LLMs) across various\nregions underscores the urgent need to evaluate their alignment with human\nvalues. Current benchmarks, however, fall short of effectively uncovering\nsafety vulnerabilities in LLMs. Despite numerous models achieving high scores\nand 'topping the chart' in these evaluations, there is still a significant gap\nin LLMs' deeper alignment with human values and achieving genuine harmlessness.\nTo this end, this paper proposes a value alignment benchmark named Flames,\nwhich encompasses both common harmlessness principles and a unique morality\ndimension that integrates specific Chinese values such as harmony. Accordingly,\nwe carefully design adversarial prompts that incorporate complex scenarios and\njailbreaking methods, mostly with implicit malice. By prompting 17 mainstream\nLLMs, we obtain model responses and rigorously annotate them for detailed\nevaluation. Our findings indicate that all the evaluated LLMs demonstrate\nrelatively poor performance on Flames, particularly in the safety and fairness\ndimensions. We also develop a lightweight specified scorer capable of scoring\nLLMs across multiple dimensions to efficiently evaluate new models on the\nbenchmark. The complexity of Flames has far exceeded existing benchmarks,\nsetting a new challenge for contemporary LLMs and highlighting the need for\nfurther alignment of LLMs. Our benchmark is publicly available at\nhttps://github.com/AIFlames/Flames.", + "authors": "Kexin Huang, Xiangyang Liu, Qianyu Guo, Tianxiang Sun, Jiawei Sun, Yaru Wang, Zeyang Zhou, Yixu Wang, Yan Teng, Xipeng Qiu, Yingchun Wang, Dahua Lin", + "published": "2023-11-12", + "updated": "2024-04-15", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.00884v2", + "title": "Text classification of column headers with a controlled vocabulary: leveraging LLMs for metadata enrichment", + "abstract": "Traditional dataset retrieval systems index on metadata information rather\nthan on the data values. Thus relying primarily on manual annotations and\nhigh-quality metadata, processes known to be labour-intensive and challenging\nto automate. We propose a method to support metadata enrichment with topic\nannotations of column headers using three Large Language Models (LLMs):\nChatGPT-3.5, GoogleBard and GoogleGemini. We investigate the LLMs ability to\nclassify column headers based on domain-specific topics from a controlled\nvocabulary. We evaluate our approach by assessing the internal consistency of\nthe LLMs, the inter-machine alignment, and the human-machine agreement for the\ntopic classification task. Additionally, we investigate the impact of\ncontextual information (i.e. dataset description) on the classification\noutcomes. Our results suggest that ChatGPT and GoogleGemini outperform\nGoogleBard for internal consistency as well as LLM-human-alignment.\nInterestingly, we found that context had no impact on the LLMs performances.\nThis work proposes a novel approach that leverages LLMs for text classification\nusing a controlled topic vocabulary, which has the potential to facilitate\nautomated metadata enrichment, thereby enhancing dataset retrieval and the\nFindability, Accessibility, Interoperability and Reusability (FAIR) of research\ndata on the Web.", + "authors": "Margherita Martorana, Tobias Kuhn, Lise Stork, Jacco van Ossenbruggen", + "published": "2024-03-01", + "updated": "2024-03-05", + "primary_cat": "cs.DB", + "cats": [ + "cs.DB", + "cs.AI", + "cs.IR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.17553v1", + "title": "RuBia: A Russian Language Bias Detection Dataset", + "abstract": "Warning: this work contains upsetting or disturbing content.\n Large language models (LLMs) tend to learn the social and cultural biases\npresent in the raw pre-training data. To test if an LLM's behavior is fair,\nfunctional datasets are employed, and due to their purpose, these datasets are\nhighly language and culture-specific. In this paper, we address a gap in the\nscope of multilingual bias evaluation by presenting a bias detection dataset\nspecifically designed for the Russian language, dubbed as RuBia. The RuBia\ndataset is divided into 4 domains: gender, nationality, socio-economic status,\nand diverse, each of the domains is further divided into multiple fine-grained\nsubdomains. Every example in the dataset consists of two sentences with the\nfirst reinforcing a potentially harmful stereotype or trope and the second\ncontradicting it. These sentence pairs were first written by volunteers and\nthen validated by native-speaking crowdsourcing workers. Overall, there are\nnearly 2,000 unique sentence pairs spread over 19 subdomains in RuBia. To\nillustrate the dataset's purpose, we conduct a diagnostic evaluation of\nstate-of-the-art or near-state-of-the-art LLMs and discuss the LLMs'\npredisposition to social biases.", + "authors": "Veronika Grigoreva, Anastasiia Ivanova, Ilseyar Alimova, Ekaterina Artemova", + "published": "2024-03-26", + "updated": "2024-03-26", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.07420v1", + "title": "FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs", + "abstract": "Training large language models (LLMs) is a costly endeavour in terms of time\nand computational resources. The large amount of training data used during the\nunsupervised pre-training phase makes it difficult to verify all data and,\nunfortunately, undesirable data may be ingested during training. Re-training\nfrom scratch is impractical and has led to the creation of the 'unlearning'\ndiscipline where models are modified to \"unlearn\" undesirable information\nwithout retraining. However, any modification can alter the behaviour of LLMs,\nespecially on key dimensions such as fairness. This is the first work that\nexamines this interplay between unlearning and fairness for LLMs. In\nparticular, we focus on a popular unlearning framework known as SISA [Bourtoule\net al., 2021], which creates an ensemble of models trained on disjoint shards.\nWe evaluate the performance-fairness trade-off for SISA, and empirically\ndemsontrate that SISA can indeed reduce fairness in LLMs. To remedy this, we\npropose post-processing bias mitigation techniques for ensemble models produced\nby SISA. We adapt the post-processing fairness improvement technique from\n[Hardt et al., 2016] to design three methods that can handle model ensembles,\nand prove that one of the methods is an optimal fair predictor for ensemble of\nmodels. Through experimental results, we demonstrate the efficacy of our\npost-processing framework called 'FairSISA'.", + "authors": "Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat, Nathalie Baracaldo", + "published": "2023-12-12", + "updated": "2023-12-12", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.15451v1", + "title": "Towards Enabling FAIR Dataspaces Using Large Language Models", + "abstract": "Dataspaces have recently gained adoption across various sectors, including\ntraditionally less digitized domains such as culture. Leveraging Semantic Web\ntechnologies helps to make dataspaces FAIR, but their complexity poses a\nsignificant challenge to the adoption of dataspaces and increases their cost.\nThe advent of Large Language Models (LLMs) raises the question of how these\nmodels can support the adoption of FAIR dataspaces. In this work, we\ndemonstrate the potential of LLMs in dataspaces with a concrete example. We\nalso derive a research agenda for exploring this emerging field.", + "authors": "Benedikt T. Arnold, Johannes Theissen-Lipp, Diego Collarana, Christoph Lange, Sandra Geisler, Edward Curry, Stefan Decker", + "published": "2024-03-18", + "updated": "2024-03-18", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2401.08495v2", + "title": "Large Language Models Portray Socially Subordinate Groups as More Homogeneous, Consistent with a Bias Observed in Humans", + "abstract": "Large language models (LLMs) are becoming pervasive in everyday life, yet\ntheir propensity to reproduce biases inherited from training data remains a\npressing concern. Prior investigations into bias in LLMs have focused on the\nassociation of social groups with stereotypical attributes. However, this is\nonly one form of human bias such systems may reproduce. We investigate a new\nform of bias in LLMs that resembles a social psychological phenomenon where\nsocially subordinate groups are perceived as more homogeneous than socially\ndominant groups. We had ChatGPT, a state-of-the-art LLM, generate texts about\nintersectional group identities and compared those texts on measures of\nhomogeneity. We consistently found that ChatGPT portrayed African, Asian, and\nHispanic Americans as more homogeneous than White Americans, indicating that\nthe model described racial minority groups with a narrower range of human\nexperience. ChatGPT also portrayed women as more homogeneous than men, but\nthese differences were small. Finally, we found that the effect of gender\ndiffered across racial/ethnic groups such that the effect of gender was\nconsistent within African and Hispanic Americans but not within Asian and White\nAmericans. We argue that the tendency of LLMs to describe groups as less\ndiverse risks perpetuating stereotypes and discriminatory behavior.", + "authors": "Messi H. J. Lee, Jacob M. Montgomery, Calvin K. Lai", + "published": "2024-01-16", + "updated": "2024-04-26", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2309.09397v1", + "title": "Do Large GPT Models Discover Moral Dimensions in Language Representations? A Topological Study Of Sentence Embeddings", + "abstract": "As Large Language Models are deployed within Artificial Intelligence systems,\nthat are increasingly integrated with human society, it becomes more important\nthan ever to study their internal structures. Higher level abilities of LLMs\nsuch as GPT-3.5 emerge in large part due to informative language\nrepresentations they induce from raw text data during pre-training on trillions\nof words. These embeddings exist in vector spaces of several thousand\ndimensions, and their processing involves mapping between multiple vector\nspaces, with total number of parameters on the order of trillions. Furthermore,\nthese language representations are induced by gradient optimization, resulting\nin a black box system that is hard to interpret. In this paper, we take a look\nat the topological structure of neuronal activity in the \"brain\" of Chat-GPT's\nfoundation language model, and analyze it with respect to a metric representing\nthe notion of fairness. We develop a novel approach to visualize GPT's moral\ndimensions. We first compute a fairness metric, inspired by social psychology\nliterature, to identify factors that typically influence fairness assessments\nin humans, such as legitimacy, need, and responsibility. Subsequently, we\nsummarize the manifold's shape using a lower-dimensional simplicial complex,\nwhose topology is derived from this metric. We color it with a heat map\nassociated with this fairness metric, producing human-readable visualizations\nof the high-dimensional sentence manifold. Our results show that sentence\nembeddings based on GPT-3.5 can be decomposed into two submanifolds\ncorresponding to fair and unfair moral judgments. This indicates that GPT-based\nlanguage models develop a moral dimension within their representation spaces\nand induce an understanding of fairness during their training process.", + "authors": "Stephen Fitz", + "published": "2023-09-17", + "updated": "2023-09-17", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY", + "cs.LG", + "cs.NE" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.18569v1", + "title": "Fairness of ChatGPT", + "abstract": "Understanding and addressing unfairness in LLMs are crucial for responsible\nAI deployment. However, there is a limited availability of quantitative\nanalyses and in-depth studies regarding fairness evaluations in LLMs,\nespecially when applying LLMs to high-stakes fields. This work aims to fill\nthis gap by providing a systematic evaluation of the effectiveness and fairness\nof LLMs using ChatGPT as a study case. We focus on assessing ChatGPT's\nperformance in high-takes fields including education, criminology, finance and\nhealthcare. To make thorough evaluation, we consider both group fairness and\nindividual fairness and we also observe the disparities in ChatGPT's outputs\nunder a set of biased or unbiased prompts. This work contributes to a deeper\nunderstanding of LLMs' fairness performance, facilitates bias mitigation and\nfosters the development of responsible artificial intelligence systems.", + "authors": "Yunqi Li, Yongfeng Zhang", + "published": "2023-05-22", + "updated": "2023-05-22", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.AI", + "cs.CL", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2309.08836v2", + "title": "Bias and Fairness in Chatbots: An Overview", + "abstract": "Chatbots have been studied for more than half a century. With the rapid\ndevelopment of natural language processing (NLP) technologies in recent years,\nchatbots using large language models (LLMs) have received much attention\nnowadays. Compared with traditional ones, modern chatbots are more powerful and\nhave been used in real-world applications. There are however, bias and fairness\nconcerns in modern chatbot design. Due to the huge amounts of training data,\nextremely large model sizes, and lack of interpretability, bias mitigation and\nfairness preservation of modern chatbots are challenging. Thus, a comprehensive\noverview on bias and fairness in chatbot systems is given in this paper. The\nhistory of chatbots and their categories are first reviewed. Then, bias sources\nand potential harms in applications are analyzed. Considerations in designing\nfair and unbiased chatbot systems are examined. Finally, future research\ndirections are discussed.", + "authors": "Jintang Xue, Yun-Cheng Wang, Chengwei Wei, Xiaofeng Liu, Jonghye Woo, C. -C. Jay Kuo", + "published": "2023-09-16", + "updated": "2023-12-10", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.03033v1", + "title": "Beyond Words: A Mathematical Framework for Interpreting Large Language Models", + "abstract": "Large language models (LLMs) are powerful AI tools that can generate and\ncomprehend natural language text and other complex information. However, the\nfield lacks a mathematical framework to systematically describe, compare and\nimprove LLMs. We propose Hex a framework that clarifies key terms and concepts\nin LLM research, such as hallucinations, alignment, self-verification and\nchain-of-thought reasoning. The Hex framework offers a precise and consistent\nway to characterize LLMs, identify their strengths and weaknesses, and\nintegrate new findings. Using Hex, we differentiate chain-of-thought reasoning\nfrom chain-of-thought prompting and establish the conditions under which they\nare equivalent. This distinction clarifies the basic assumptions behind\nchain-of-thought prompting and its implications for methods that use it, such\nas self-verification and prompt programming.\n Our goal is to provide a formal framework for LLMs that can help both\nresearchers and practitioners explore new possibilities for generative AI. We\ndo not claim to have a definitive solution, but rather a tool for opening up\nnew research avenues. We argue that our formal definitions and results are\ncrucial for advancing the discussion on how to build generative AI systems that\nare safe, reliable, fair and robust, especially in domains like healthcare and\nsoftware engineering.", + "authors": "Javier Gonz\u00e1lez, Aditya V. Nori", + "published": "2023-11-06", + "updated": "2023-11-06", + "primary_cat": "cs.LG", + "cats": [ + "cs.LG", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.02049v1", + "title": "Post Turing: Mapping the landscape of LLM Evaluation", + "abstract": "In the rapidly evolving landscape of Large Language Models (LLMs),\nintroduction of well-defined and standardized evaluation methodologies remains\na crucial challenge. This paper traces the historical trajectory of LLM\nevaluations, from the foundational questions posed by Alan Turing to the modern\nera of AI research. We categorize the evolution of LLMs into distinct periods,\neach characterized by its unique benchmarks and evaluation criteria. As LLMs\nincreasingly mimic human-like behaviors, traditional evaluation proxies, such\nas the Turing test, have become less reliable. We emphasize the pressing need\nfor a unified evaluation system, given the broader societal implications of\nthese models. Through an analysis of common evaluation methodologies, we\nadvocate for a qualitative shift in assessment approaches, underscoring the\nimportance of standardization and objective criteria. This work serves as a\ncall for the AI community to collaboratively address the challenges of LLM\nevaluation, ensuring their reliability, fairness, and societal benefit.", + "authors": "Alexey Tikhonov, Ivan P. Yamshchikov", + "published": "2023-11-03", + "updated": "2023-11-03", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "68T50", + "I.2.7" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.01937v1", + "title": "Can Large Language Models Be an Alternative to Human Evaluations?", + "abstract": "Human evaluation is indispensable and inevitable for assessing the quality of\ntexts generated by machine learning models or written by humans. However, human\nevaluation is very difficult to reproduce and its quality is notoriously\nunstable, hindering fair comparisons among different natural language\nprocessing (NLP) models and algorithms. Recently, large language models (LLMs)\nhave demonstrated exceptional performance on unseen tasks when only the task\ninstructions are provided. In this paper, we explore if such an ability of the\nLLMs can be used as an alternative to human evaluation. We present the LLMs\nwith the exact same instructions, samples to be evaluated, and questions used\nto conduct human evaluation, and then ask the LLMs to generate responses to\nthose questions; we dub this LLM evaluation. We use human evaluation and LLM\nevaluation to evaluate the texts in two NLP tasks: open-ended story generation\nand adversarial attacks. We show that the result of LLM evaluation is\nconsistent with the results obtained by expert human evaluation: the texts\nrated higher by human experts are also rated higher by the LLMs. We also find\nthat the results of LLM evaluation are stable over different formatting of the\ntask instructions and the sampling algorithm used to generate the answer. We\nare the first to show the potential of using LLMs to assess the quality of\ntexts and discuss the limitations and ethical considerations of LLM evaluation.", + "authors": "Cheng-Han Chiang, Hung-yi Lee", + "published": "2023-05-03", + "updated": "2023-05-03", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.HC" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.12090v1", + "title": "UP5: Unbiased Foundation Model for Fairness-aware Recommendation", + "abstract": "Recent advancements in foundation models such as large language models (LLM)\nhave propelled them to the forefront of recommender systems (RS). Moreover,\nfairness in RS is critical since many users apply it for decision-making and\ndemand fulfillment. However, at present, there is a lack of understanding\nregarding the level of fairness exhibited by recommendation foundation models\nand the appropriate methods for equitably treating different groups of users in\nfoundation models. In this paper, we focus on user-side unfairness problem and\nshow through a thorough examination that there is unfairness involved in LLMs\nthat lead to unfair recommendation results. To eliminate bias from LLM for\nfairness-aware recommendation, we introduce a novel Unbiased P5 (UP5)\nfoundation model based on Counterfactually-Fair-Prompting (CFP) techniques. CFP\nincludes two sub-modules: a personalized prefix prompt that enhances fairness\nwith respect to individual sensitive attributes, and a Prompt Mixture that\nintegrates multiple counterfactually-fair prompts for a set of sensitive\nattributes. Experiments are conducted on two real-world datasets, MovieLens-1M\nand Insurance, and results are compared with both matching-based and\nsequential-based fairness-aware recommendation models. The results show that\nUP5 achieves better recommendation performance and meanwhile exhibits a high\nlevel of fairness.", + "authors": "Wenyue Hua, Yingqiang Ge, Shuyuan Xu, Jianchao Ji, Yongfeng Zhang", + "published": "2023-05-20", + "updated": "2023-05-20", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR", + "cs.AI", + "cs.CL", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2401.15585v1", + "title": "Evaluating Gender Bias in Large Language Models via Chain-of-Thought Prompting", + "abstract": "There exist both scalable tasks, like reading comprehension and\nfact-checking, where model performance improves with model size, and unscalable\ntasks, like arithmetic reasoning and symbolic reasoning, where model\nperformance does not necessarily improve with model size. Large language models\n(LLMs) equipped with Chain-of-Thought (CoT) prompting are able to make accurate\nincremental predictions even on unscalable tasks. Unfortunately, despite their\nexceptional reasoning abilities, LLMs tend to internalize and reproduce\ndiscriminatory societal biases. Whether CoT can provide discriminatory or\negalitarian rationalizations for the implicit information in unscalable tasks\nremains an open question.\n In this study, we examine the impact of LLMs' step-by-step predictions on\ngender bias in unscalable tasks. For this purpose, we construct a benchmark for\nan unscalable task where the LLM is given a list of words comprising feminine,\nmasculine, and gendered occupational words, and is required to count the number\nof feminine and masculine words. In our CoT prompts, we require the LLM to\nexplicitly indicate whether each word in the word list is a feminine or\nmasculine before making the final predictions. With counting and handling the\nmeaning of words, this benchmark has characteristics of both arithmetic\nreasoning and symbolic reasoning. Experimental results in English show that\nwithout step-by-step prediction, most LLMs make socially biased predictions,\ndespite the task being as simple as counting words. Interestingly, CoT\nprompting reduces this unconscious social bias in LLMs and encourages fair\npredictions.", + "authors": "Masahiro Kaneko, Danushka Bollegala, Naoaki Okazaki, Timothy Baldwin", + "published": "2024-01-28", + "updated": "2024-01-28", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.08517v1", + "title": "Online Safety Analysis for LLMs: a Benchmark, an Assessment, and a Path Forward", + "abstract": "While Large Language Models (LLMs) have seen widespread applications across\nnumerous fields, their limited interpretability poses concerns regarding their\nsafe operations from multiple aspects, e.g., truthfulness, robustness, and\nfairness. Recent research has started developing quality assurance methods for\nLLMs, introducing techniques such as offline detector-based or uncertainty\nestimation methods. However, these approaches predominantly concentrate on\npost-generation analysis, leaving the online safety analysis for LLMs during\nthe generation phase an unexplored area. To bridge this gap, we conduct in this\nwork a comprehensive evaluation of the effectiveness of existing online safety\nanalysis methods on LLMs. We begin with a pilot study that validates the\nfeasibility of detecting unsafe outputs in the early generation process.\nFollowing this, we establish the first publicly available benchmark of online\nsafety analysis for LLMs, including a broad spectrum of methods, models, tasks,\ndatasets, and evaluation metrics. Utilizing this benchmark, we extensively\nanalyze the performance of state-of-the-art online safety analysis methods on\nboth open-source and closed-source LLMs. This analysis reveals the strengths\nand weaknesses of individual methods and offers valuable insights into\nselecting the most appropriate method based on specific application scenarios\nand task requirements. Furthermore, we also explore the potential of using\nhybridization methods, i.e., combining multiple methods to derive a collective\nsafety conclusion, to enhance the efficacy of online safety analysis for LLMs.\nOur findings indicate a promising direction for the development of innovative\nand trustworthy quality assurance methodologies for LLMs, facilitating their\nreliable deployments across diverse domains.", + "authors": "Xuan Xie, Jiayang Song, Zhehua Zhou, Yuheng Huang, Da Song, Lei Ma", + "published": "2024-04-12", + "updated": "2024-04-12", + "primary_cat": "cs.SE", + "cats": [ + "cs.SE", + "cs.AI", + "cs.CL", + "cs.CR", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.09606v1", + "title": "Large Language Models and Causal Inference in Collaboration: A Comprehensive Survey", + "abstract": "Causal inference has shown potential in enhancing the predictive accuracy,\nfairness, robustness, and explainability of Natural Language Processing (NLP)\nmodels by capturing causal relationships among variables. The emergence of\ngenerative Large Language Models (LLMs) has significantly impacted various NLP\ndomains, particularly through their advanced reasoning capabilities. This\nsurvey focuses on evaluating and improving LLMs from a causal view in the\nfollowing areas: understanding and improving the LLMs' reasoning capacity,\naddressing fairness and safety issues in LLMs, complementing LLMs with\nexplanations, and handling multimodality. Meanwhile, LLMs' strong reasoning\ncapacities can in turn contribute to the field of causal inference by aiding\ncausal relationship discovery and causal effect estimations. This review\nexplores the interplay between causal inference frameworks and LLMs from both\nperspectives, emphasizing their collective potential to further the development\nof more advanced and equitable artificial intelligence systems.", + "authors": "Xiaoyu Liu, Paiheng Xu, Junda Wu, Jiaxin Yuan, Yifan Yang, Yuhang Zhou, Fuxiao Liu, Tianrui Guan, Haoliang Wang, Tong Yu, Julian McAuley, Wei Ai, Furong Huang", + "published": "2024-03-14", + "updated": "2024-03-14", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.02294v1", + "title": "LLMs grasp morality in concept", + "abstract": "Work in AI ethics and fairness has made much progress in regulating LLMs to\nreflect certain values, such as fairness, truth, and diversity. However, it has\ntaken the problem of how LLMs might 'mean' anything at all for granted. Without\naddressing this, it is not clear what imbuing LLMs with such values even means.\nIn response, we provide a general theory of meaning that extends beyond humans.\nWe use this theory to explicate the precise nature of LLMs as meaning-agents.\nWe suggest that the LLM, by virtue of its position as a meaning-agent, already\ngrasps the constructions of human society (e.g. morality, gender, and race) in\nconcept. Consequently, under certain ethical frameworks, currently popular\nmethods for model alignment are limited at best and counterproductive at worst.\nMoreover, unaligned models may help us better develop our moral and social\nphilosophy.", + "authors": "Mark Pock, Andre Ye, Jared Moore", + "published": "2023-11-04", + "updated": "2023-11-04", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.15215v1", + "title": "Item-side Fairness of Large Language Model-based Recommendation System", + "abstract": "Recommendation systems for Web content distribution intricately connect to\nthe information access and exposure opportunities for vulnerable populations.\nThe emergence of Large Language Models-based Recommendation System (LRS) may\nintroduce additional societal challenges to recommendation systems due to the\ninherent biases in Large Language Models (LLMs). From the perspective of\nitem-side fairness, there remains a lack of comprehensive investigation into\nthe item-side fairness of LRS given the unique characteristics of LRS compared\nto conventional recommendation systems. To bridge this gap, this study examines\nthe property of LRS with respect to item-side fairness and reveals the\ninfluencing factors of both historical users' interactions and inherent\nsemantic biases of LLMs, shedding light on the need to extend conventional\nitem-side fairness methods for LRS. Towards this goal, we develop a concise and\neffective framework called IFairLRS to enhance the item-side fairness of an\nLRS. IFairLRS covers the main stages of building an LRS with specifically\nadapted strategies to calibrate the recommendations of LRS. We utilize IFairLRS\nto fine-tune LLaMA, a representative LLM, on \\textit{MovieLens} and\n\\textit{Steam} datasets, and observe significant item-side fairness\nimprovements. The code can be found in\nhttps://github.com/JiangM-C/IFairLRS.git.", + "authors": "Meng Jiang, Keqin Bao, Jizhi Zhang, Wenjie Wang, Zhengyi Yang, Fuli Feng, Xiangnan He", + "published": "2024-02-23", + "updated": "2024-02-23", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.10199v3", + "title": "CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting", + "abstract": "As the utilization of large language models (LLMs) has proliferated\nworldwide, it is crucial for them to have adequate knowledge and fair\nrepresentation for diverse global cultures. In this work, we uncover culture\nperceptions of three SOTA models on 110 countries and regions on 8\nculture-related topics through culture-conditioned generations, and extract\nsymbols from these generations that are associated to each culture by the LLM.\nWe discover that culture-conditioned generation consist of linguistic \"markers\"\nthat distinguish marginalized cultures apart from default cultures. We also\ndiscover that LLMs have an uneven degree of diversity in the culture symbols,\nand that cultures from different geographic regions have different presence in\nLLMs' culture-agnostic generation. Our findings promote further research in\nstudying the knowledge and fairness of global culture perception in LLMs. Code\nand Data can be found in: https://github.com/huihanlhh/Culture-Gen/", + "authors": "Huihan Li, Liwei Jiang, Jena D. Huang, Hyunwoo Kim, Sebastin Santy, Taylor Sorensen, Bill Yuchen Lin, Nouha Dziri, Xiang Ren, Yejin Choi", + "published": "2024-04-16", + "updated": "2024-04-26", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2309.03852v2", + "title": "FLM-101B: An Open LLM and How to Train It with $100K Budget", + "abstract": "Large language models (LLMs) have achieved remarkable success in NLP and\nmultimodal tasks, among others. Despite these successes, two main challenges\nremain in developing LLMs: (i) high computational cost, and (ii) fair and\nobjective evaluations. In this paper, we report a solution to significantly\nreduce LLM training cost through a growth strategy. We demonstrate that a\n101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US\ndollars. Inspired by IQ tests, we also consolidate an additional range of\nevaluations on top of existing evaluations that focus on knowledge-oriented\nabilities. These IQ evaluations include symbolic mapping, rule understanding,\npattern mining, and anti-interference. Such evaluations minimize the potential\nimpact of memorization. Experimental results show that our model, named\nFLM-101B, trained with a budget of 100K US dollars, achieves performance\ncomparable to powerful and well-known models, e.g., GPT-3 and GLM-130B,\nespecially on the additional range of IQ evaluations. The checkpoint of\nFLM-101B is released at https://huggingface.co/CofeAI/FLM-101B.", + "authors": "Xiang Li, Yiqun Yao, Xin Jiang, Xuezhi Fang, Xuying Meng, Siqi Fan, Peng Han, Jing Li, Li Du, Bowen Qin, Zheng Zhang, Aixin Sun, Yequan Wang", + "published": "2023-09-07", + "updated": "2023-09-17", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.18580v1", + "title": "FFT: Towards Harmlessness Evaluation and Analysis for LLMs with Factuality, Fairness, Toxicity", + "abstract": "The widespread of generative artificial intelligence has heightened concerns\nabout the potential harms posed by AI-generated texts, primarily stemming from\nfactoid, unfair, and toxic content. Previous researchers have invested much\neffort in assessing the harmlessness of generative language models. However,\nexisting benchmarks are struggling in the era of large language models (LLMs),\ndue to the stronger language generation and instruction following capabilities,\nas well as wider applications. In this paper, we propose FFT, a new benchmark\nwith 2116 elaborated-designed instances, for LLM harmlessness evaluation with\nfactuality, fairness, and toxicity. To investigate the potential harms of LLMs,\nwe evaluate 9 representative LLMs covering various parameter scales, training\nstages, and creators. Experiments show that the harmlessness of LLMs is still\nunder-satisfactory, and extensive analysis derives some insightful findings\nthat could inspire future research for harmless LLM research.", + "authors": "Shiyao Cui, Zhenyu Zhang, Yilong Chen, Wenyuan Zhang, Tianyun Liu, Siqi Wang, Tingwen Liu", + "published": "2023-11-30", + "updated": "2023-11-30", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.CR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.03192v1", + "title": "Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers", + "abstract": "The integration of Large Language Models (LLMs) in information retrieval has\nraised a critical reevaluation of fairness in the text-ranking models. LLMs,\nsuch as GPT models and Llama2, have shown effectiveness in natural language\nunderstanding tasks, and prior works (e.g., RankGPT) have also demonstrated\nthat the LLMs exhibit better performance than the traditional ranking models in\nthe ranking task. However, their fairness remains largely unexplored. This\npaper presents an empirical study evaluating these LLMs using the TREC Fair\nRanking dataset, focusing on the representation of binary protected attributes\nsuch as gender and geographic location, which are historically underrepresented\nin search outcomes. Our analysis delves into how these LLMs handle queries and\ndocuments related to these attributes, aiming to uncover biases in their\nranking algorithms. We assess fairness from both user and content perspectives,\ncontributing an empirical benchmark for evaluating LLMs as the fair ranker.", + "authors": "Yuan Wang, Xuyang Wu, Hsin-Tai Wu, Zhiqiang Tao, Yi Fang", + "published": "2024-04-04", + "updated": "2024-04-04", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR", + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.06056v1", + "title": "METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities", + "abstract": "Large-Language Models (LLMs) have shifted the paradigm of natural language\ndata processing. However, their black-boxed and probabilistic characteristics\ncan lead to potential risks in the quality of outputs in diverse LLM\napplications. Recent studies have tested Quality Attributes (QAs), such as\nrobustness or fairness, of LLMs by generating adversarial input texts. However,\nexisting studies have limited their coverage of QAs and tasks in LLMs and are\ndifficult to extend. Additionally, these studies have only used one evaluation\nmetric, Attack Success Rate (ASR), to assess the effectiveness of their\napproaches. We propose a MEtamorphic Testing for Analyzing LLMs (METAL)\nframework to address these issues by applying Metamorphic Testing (MT)\ntechniques. This approach facilitates the systematic testing of LLM qualities\nby defining Metamorphic Relations (MRs), which serve as modularized evaluation\nmetrics. The METAL framework can automatically generate hundreds of MRs from\ntemplates that cover various QAs and tasks. In addition, we introduced novel\nmetrics that integrate the ASR method into the semantic qualities of text to\nassess the effectiveness of MRs accurately. Through the experiments conducted\nwith three prominent LLMs, we have confirmed that the METAL framework\neffectively evaluates essential QAs on primary LLM tasks and reveals the\nquality risks in LLMs. Moreover, the newly proposed metrics can guide the\noptimal MRs for testing each task and suggest the most effective method for\ngenerating MRs.", + "authors": "Sangwon Hyun, Mingyu Guo, M. Ali Babar", + "published": "2023-12-11", + "updated": "2023-12-11", + "primary_cat": "cs.SE", + "cats": [ + "cs.SE", + "cs.AI", + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.14473v1", + "title": "The Ethics of ChatGPT in Medicine and Healthcare: A Systematic Review on Large Language Models (LLMs)", + "abstract": "With the introduction of ChatGPT, Large Language Models (LLMs) have received\nenormous attention in healthcare. Despite their potential benefits, researchers\nhave underscored various ethical implications. While individual instances have\ndrawn much attention, the debate lacks a systematic overview of practical\napplications currently researched and ethical issues connected to them. Against\nthis background, this work aims to map the ethical landscape surrounding the\ncurrent stage of deployment of LLMs in medicine and healthcare. Electronic\ndatabases and preprint servers were queried using a comprehensive search\nstrategy. Studies were screened and extracted following a modified rapid review\napproach. Methodological quality was assessed using a hybrid approach. For 53\nrecords, a meta-aggregative synthesis was performed. Four fields of\napplications emerged and testify to a vivid exploration phase. Advantages of\nusing LLMs are attributed to their capacity in data analysis, personalized\ninformation provisioning, support in decision-making, mitigating information\nloss and enhancing information accessibility. However, we also identifies\nrecurrent ethical concerns connected to fairness, bias, non-maleficence,\ntransparency, and privacy. A distinctive concern is the tendency to produce\nharmful misinformation or convincingly but inaccurate content. A recurrent plea\nfor ethical guidance and human oversight is evident. Given the variety of use\ncases, it is suggested that the ethical guidance debate be reframed to focus on\ndefining what constitutes acceptable human oversight across the spectrum of\napplications. This involves considering diverse settings, varying potentials\nfor harm, and different acceptable thresholds for performance and certainty in\nhealthcare. In addition, a critical inquiry is necessary to determine the\nextent to which the current experimental use of LLMs is necessary and\njustified.", + "authors": "Joschka Haltaufderheide, Robert Ranisch", + "published": "2024-03-21", + "updated": "2024-03-21", + "primary_cat": "cs.CY", + "cats": [ + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.08656v1", + "title": "Linear Cross-document Event Coreference Resolution with X-AMR", + "abstract": "Event Coreference Resolution (ECR) as a pairwise mention classification task\nis expensive both for automated systems and manual annotations. The task's\nquadratic difficulty is exacerbated when using Large Language Models (LLMs),\nmaking prompt engineering for ECR prohibitively costly. In this work, we\npropose a graphical representation of events, X-AMR, anchored around individual\nmentions using a \\textbf{cross}-document version of \\textbf{A}bstract\n\\textbf{M}eaning \\textbf{R}epresentation. We then linearize the ECR with a\nnovel multi-hop coreference algorithm over the event graphs. The event graphs\nsimplify ECR, making it a) LLM cost-effective, b) compositional and\ninterpretable, and c) easily annotated. For a fair assessment, we first enrich\nan existing ECR benchmark dataset with these event graphs using an\nannotator-friendly tool we introduce. Then, we employ GPT-4, the newest LLM by\nOpenAI, for these annotations. Finally, using the ECR algorithm, we assess\nGPT-4 against humans and analyze its limitations. Through this research, we aim\nto advance the state-of-the-art for efficient ECR and shed light on the\npotential shortcomings of current LLMs at this task. Code and annotations:\n\\url{https://github.com/ahmeshaf/gpt_coref}", + "authors": "Shafiuddin Rehan Ahmed, George Arthur Baker, Evi Judge, Michael Regan, Kristin Wright-Bettner, Martha Palmer, James H. Martin", + "published": "2024-03-25", + "updated": "2024-03-25", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2308.05374v2", + "title": "Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment", + "abstract": "Ensuring alignment, which refers to making models behave in accordance with\nhuman intentions [1,2], has become a critical task before deploying large\nlanguage models (LLMs) in real-world applications. For instance, OpenAI devoted\nsix months to iteratively aligning GPT-4 before its release [3]. However, a\nmajor challenge faced by practitioners is the lack of clear guidance on\nevaluating whether LLM outputs align with social norms, values, and\nregulations. This obstacle hinders systematic iteration and deployment of LLMs.\nTo address this issue, this paper presents a comprehensive survey of key\ndimensions that are crucial to consider when assessing LLM trustworthiness. The\nsurvey covers seven major categories of LLM trustworthiness: reliability,\nsafety, fairness, resistance to misuse, explainability and reasoning, adherence\nto social norms, and robustness. Each major category is further divided into\nseveral sub-categories, resulting in a total of 29 sub-categories.\nAdditionally, a subset of 8 sub-categories is selected for further\ninvestigation, where corresponding measurement studies are designed and\nconducted on several widely-used LLMs. The measurement results indicate that,\nin general, more aligned models tend to perform better in terms of overall\ntrustworthiness. However, the effectiveness of alignment varies across the\ndifferent trustworthiness categories considered. This highlights the importance\nof conducting more fine-grained analyses, testing, and making continuous\nimprovements on LLM alignment. By shedding light on these key dimensions of LLM\ntrustworthiness, this paper aims to provide valuable insights and guidance to\npractitioners in the field. Understanding and addressing these concerns will be\ncrucial in achieving reliable and ethically sound deployment of LLMs in various\napplications.", + "authors": "Yang Liu, Yuanshun Yao, Jean-Francois Ton, Xiaoying Zhang, Ruocheng Guo, Hao Cheng, Yegor Klochkov, Muhammad Faaiz Taufiq, Hang Li", + "published": "2023-08-10", + "updated": "2024-03-21", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2312.15398v1", + "title": "Fairness-Aware Structured Pruning in Transformers", + "abstract": "The increasing size of large language models (LLMs) has introduced challenges\nin their training and inference. Removing model components is perceived as a\nsolution to tackle the large model sizes, however, existing pruning methods\nsolely focus on performance, without considering an essential aspect for the\nresponsible use of LLMs: model fairness. It is crucial to address the fairness\nof LLMs towards diverse groups, such as women, Black people, LGBTQ+, Jewish\ncommunities, among others, as they are being deployed and available to a wide\naudience. In this work, first, we investigate how attention heads impact\nfairness and performance in pre-trained transformer-based language models. We\nthen propose a novel method to prune the attention heads that negatively impact\nfairness while retaining the heads critical for performance, i.e. language\nmodeling capabilities. Our approach is practical in terms of time and\nresources, as it does not require fine-tuning the final pruned, and fairer,\nmodel. Our findings demonstrate a reduction in gender bias by 19%, 19.5%,\n39.5%, 34.7%, 23%, and 8% for DistilGPT-2, GPT-2, GPT-Neo of two different\nsizes, GPT-J, and Llama 2 models, respectively, in comparison to the biased\nmodel, with only a slight decrease in performance.", + "authors": "Abdelrahman Zayed, Goncalo Mordido, Samira Shabanian, Ioana Baldini, Sarath Chandar", + "published": "2023-12-24", + "updated": "2023-12-24", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.CY", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.13095v1", + "title": "Enhancing Logical Reasoning in Large Language Models to Facilitate Legal Applications", + "abstract": "Language serves as a vehicle for conveying thought, enabling communication\namong individuals. The ability to distinguish between diverse concepts,\nidentify fairness and injustice, and comprehend a range of legal notions\nfundamentally relies on logical reasoning. Large Language Models (LLMs) attempt\nto emulate human language understanding and generation, but their competency in\nlogical reasoning remains limited. This paper seeks to address the\nphilosophical question: How can we effectively teach logical reasoning to LLMs\nwhile maintaining a deep understanding of the intricate relationship between\nlanguage and logic? By focusing on bolstering LLMs' capabilities in logical\nreasoning, we aim to expand their applicability in law and other\nlogic-intensive disciplines. To this end, we propose a Reinforcement Learning\nfrom Logical Feedback (RLLF) approach, which serves as a potential framework\nfor refining LLMs' reasoning capacities. Through RLLF and a revised evaluation\nmethodology, we explore new avenues for research in this domain and contribute\nto the development of LLMs capable of handling complex legal reasoning tasks\nwhile acknowledging the fundamental connection between language and logic.", + "authors": "Ha-Thanh Nguyen, Wachara Fungwacharakorn, Ken Satoh", + "published": "2023-11-22", + "updated": "2023-11-22", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.04814v2", + "title": "Evaluation of LLMs on Syntax-Aware Code Fill-in-the-Middle Tasks", + "abstract": "We introduce Syntax-Aware Fill-In-the-Middle (SAFIM), a new benchmark for\nevaluating Large Language Models (LLMs) on the code Fill-in-the-Middle (FIM)\ntask. This benchmark focuses on syntax-aware completions of program structures\nsuch as code blocks and conditional expressions, and includes 17,720 examples\nfrom multiple programming languages, sourced from recent code submissions after\nApril 2022 to minimize data contamination. SAFIM provides a robust framework\nwith various prompt designs and novel syntax-aware post-processing techniques,\nfacilitating accurate and fair comparisons across LLMs. Our comprehensive\nevaluation of 15 LLMs shows that FIM pretraining not only enhances FIM\nproficiency but also improves Left-to-Right (L2R) inference using LLMs. Our\nfindings challenge conventional beliefs and suggest that pretraining methods\nand data quality have more impact than model size. SAFIM thus serves as a\nfoundational platform for future research in effective pretraining strategies\nfor code LLMs. The evaluation toolkit and dataset are available at\nhttps://github.com/gonglinyuan/safim, and the leaderboard is available at\nhttps://safimbenchmark.com.", + "authors": "Linyuan Gong, Sida Wang, Mostafa Elhoushi, Alvin Cheung", + "published": "2024-03-07", + "updated": "2024-04-10", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.LG", + "cs.SE" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.10567v3", + "title": "InSaAF: Incorporating Safety through Accuracy and Fairness | Are LLMs ready for the Indian Legal Domain?", + "abstract": "Recent advancements in language technology and Artificial Intelligence have\nresulted in numerous Language Models being proposed to perform various tasks in\nthe legal domain ranging from predicting judgments to generating summaries.\nDespite their immense potential, these models have been proven to learn and\nexhibit societal biases and make unfair predictions. In this study, we explore\nthe ability of Large Language Models (LLMs) to perform legal tasks in the\nIndian landscape when social factors are involved. We present a novel metric,\n$\\beta$-weighted $\\textit{Legal Safety Score ($LSS_{\\beta}$)}$, which\nencapsulates both the fairness and accuracy aspects of the LLM. We assess LLMs'\nsafety by considering its performance in the $\\textit{Binary Statutory\nReasoning}$ task and its fairness exhibition with respect to various axes of\ndisparities in the Indian society. Task performance and fairness scores of\nLLaMA and LLaMA--2 models indicate that the proposed $LSS_{\\beta}$ metric can\neffectively determine the readiness of a model for safe usage in the legal\nsector. We also propose finetuning pipelines, utilising specialised legal\ndatasets, as a potential method to mitigate bias and improve model safety. The\nfinetuning procedures on LLaMA and LLaMA--2 models increase the $LSS_{\\beta}$,\nimproving their usability in the Indian legal domain. Our code is publicly\nreleased.", + "authors": "Yogesh Tripathi, Raghav Donakanti, Sahil Girhepuje, Ishan Kavathekar, Bhaskara Hanuma Vedula, Gokul S Krishnan, Shreya Goyal, Anmol Goel, Balaraman Ravindran, Ponnurangam Kumaraguru", + "published": "2024-02-16", + "updated": "2024-02-21", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.04892v2", + "title": "Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs", + "abstract": "Recent works have showcased the ability of LLMs to embody diverse personas in\ntheir responses, exemplified by prompts like 'You are Yoda. Explain the Theory\nof Relativity.' While this ability allows personalization of LLMs and enables\nhuman behavior simulation, its effect on LLMs' capabilities remains unclear. To\nfill this gap, we present the first extensive study of the unintended\nside-effects of persona assignment on the ability of LLMs to perform basic\nreasoning tasks. Our study covers 24 reasoning datasets, 4 LLMs, and 19 diverse\npersonas (e.g. an Asian person) spanning 5 socio-demographic groups. Our\nexperiments unveil that LLMs harbor deep rooted bias against various\nsocio-demographics underneath a veneer of fairness. While they overtly reject\nstereotypes when explicitly asked ('Are Black people less skilled at\nmathematics?'), they manifest stereotypical and erroneous presumptions when\nasked to answer questions while adopting a persona. These can be observed as\nabstentions in responses, e.g., 'As a Black person, I can't answer this\nquestion as it requires math knowledge', and generally result in a substantial\nperformance drop. Our experiments with ChatGPT-3.5 show that this bias is\nubiquitous - 80% of our personas demonstrate bias; it is significant - some\ndatasets show performance drops of 70%+; and can be especially harmful for\ncertain groups - some personas suffer statistically significant drops on 80%+\nof the datasets. Overall, all 4 LLMs exhibit this bias to varying extents, with\nGPT-4-Turbo showing the least but still a problematic amount of bias (evident\nin 42% of the personas). Further analysis shows that these persona-induced\nerrors can be hard-to-discern and hard-to-avoid. Our findings serve as a\ncautionary tale that the practice of assigning personas to LLMs - a trend on\nthe rise - can surface their deep-rooted biases and have unforeseeable and\ndetrimental side-effects.", + "authors": "Shashank Gupta, Vaishnavi Shrivastava, Ameet Deshpande, Ashwin Kalyan, Peter Clark, Ashish Sabharwal, Tushar Khot", + "published": "2023-11-08", + "updated": "2024-01-27", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2401.04057v1", + "title": "Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems", + "abstract": "The rise of generative artificial intelligence, particularly Large Language\nModels (LLMs), has intensified the imperative to scrutinize fairness alongside\naccuracy. Recent studies have begun to investigate fairness evaluations for\nLLMs within domains such as recommendations. Given that personalization is an\nintrinsic aspect of recommendation systems, its incorporation into fairness\nassessments is paramount. Yet, the degree to which current fairness evaluation\nframeworks account for personalization remains unclear. Our comprehensive\nliterature review aims to fill this gap by examining how existing frameworks\nhandle fairness evaluations of LLMs, with a focus on the integration of\npersonalization factors. Despite an exhaustive collection and analysis of\nrelevant works, we discovered that most evaluations overlook personalization, a\ncritical facet of recommendation systems, thereby inadvertently perpetuating\nunfair practices. Our findings shed light on this oversight and underscore the\nurgent need for more nuanced fairness evaluations that acknowledge\npersonalization. Such improvements are vital for fostering equitable\ndevelopment within the AI community.", + "authors": "Chandan Kumar Sah, Dr. Lian Xiaoli, Muhammad Mirajul Islam", + "published": "2024-01-08", + "updated": "2024-01-08", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR", + "cs.AI", + "cs.SE" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.08472v1", + "title": "Selecting Shots for Demographic Fairness in Few-Shot Learning with Large Language Models", + "abstract": "Recently, work in NLP has shifted to few-shot (in-context) learning, with\nlarge language models (LLMs) performing well across a range of tasks. However,\nwhile fairness evaluations have become a standard for supervised methods,\nlittle is known about the fairness of LLMs as prediction systems. Further,\ncommon standard methods for fairness involve access to models weights or are\napplied during finetuning, which are not applicable in few-shot learning. Do\nLLMs exhibit prediction biases when used for standard NLP tasks? In this work,\nwe explore the effect of shots, which directly affect the performance of\nmodels, on the fairness of LLMs as NLP classification systems. We consider how\ndifferent shot selection strategies, both existing and new demographically\nsensitive methods, affect model fairness across three standard fairness\ndatasets. We discuss how future work can include LLM fairness evaluations.", + "authors": "Carlos Aguirre, Kuleen Sasse, Isabel Cachola, Mark Dredze", + "published": "2023-11-14", + "updated": "2023-11-14", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2401.00588v1", + "title": "Fairness in Serving Large Language Models", + "abstract": "High-demand LLM inference services (e.g., ChatGPT and BARD) support a wide\nrange of requests from short chat conversations to long document reading. To\nensure that all client requests are processed fairly, most major LLM inference\nservices have request rate limits, to ensure that no client can dominate the\nrequest queue. However, this rudimentary notion of fairness also results in\nunder-utilization of the resources and poor client experience when there is\nspare capacity. While there is a rich literature on fair scheduling, serving\nLLMs presents new challenges due to their unpredictable request lengths and\ntheir unique batching characteristics on parallel accelerators. This paper\nintroduces the definition of LLM serving fairness based on a cost function that\naccounts for the number of input and output tokens processed. To achieve\nfairness in serving, we propose a novel scheduling algorithm, the Virtual Token\nCounter (VTC), a fair scheduler based on the continuous batching mechanism. We\nprove a 2x tight upper bound on the service difference between two backlogged\nclients, adhering to the requirement of work-conserving. Through extensive\nexperiments, we demonstrate the superior performance of VTC in ensuring\nfairness, especially in contrast to other baseline methods, which exhibit\nshortcomings under various conditions.", + "authors": "Ying Sheng, Shiyi Cao, Dacheng Li, Banghua Zhu, Zhuohan Li, Danyang Zhuo, Joseph E. Gonzalez, Ion Stoica", + "published": "2023-12-31", + "updated": "2023-12-31", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.LG", + "cs.PF" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2309.11653v2", + "title": "\"It's a Fair Game\", or Is It? Examining How Users Navigate Disclosure Risks and Benefits When Using LLM-Based Conversational Agents", + "abstract": "The widespread use of Large Language Model (LLM)-based conversational agents\n(CAs), especially in high-stakes domains, raises many privacy concerns.\nBuilding ethical LLM-based CAs that respect user privacy requires an in-depth\nunderstanding of the privacy risks that concern users the most. However,\nexisting research, primarily model-centered, does not provide insight into\nusers' perspectives. To bridge this gap, we analyzed sensitive disclosures in\nreal-world ChatGPT conversations and conducted semi-structured interviews with\n19 LLM-based CA users. We found that users are constantly faced with trade-offs\nbetween privacy, utility, and convenience when using LLM-based CAs. However,\nusers' erroneous mental models and the dark patterns in system design limited\ntheir awareness and comprehension of the privacy risks. Additionally, the\nhuman-like interactions encouraged more sensitive disclosures, which\ncomplicated users' ability to navigate the trade-offs. We discuss practical\ndesign guidelines and the needs for paradigm shifts to protect the privacy of\nLLM-based CA users.", + "authors": "Zhiping Zhang, Michelle Jia, Hao-Ping Lee, Bingsheng Yao, Sauvik Das, Ada Lerner, Dakuo Wang, Tianshi Li", + "published": "2023-09-20", + "updated": "2024-04-02", + "primary_cat": "cs.HC", + "cats": [ + "cs.HC", + "cs.AI", + "cs.CR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2307.15997v1", + "title": "RoCar: A Relationship Network-based Evaluation Method to Large Language Models", + "abstract": "Large language models (LLMs) have received increasing attention. However, due\nto the complexity of its capabilities, how to rationally evaluate the\ncapabilities of LLMs is still a task to be solved. We propose the RoCar method,\nwhich utilizes the defined basic schemas to randomly construct a task graph and\ngenerates natural language evaluation tasks based on the task graph to evaluate\nthe reasoning and memory abilities of LLMs respectively. Due to the very large\nrandomness of the task construction process, it is possible to ensure that none\nof the LLMs to be tested has directly learned the evaluation tasks,\nguaranteeing the fairness of the evaluation method.", + "authors": "Ming Wang, Wenfang Wu, Chongyun Gao, Daling Wang, Shi Feng, Yifei Zhang", + "published": "2023-07-29", + "updated": "2023-07-29", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2405.02219v1", + "title": "FairEvalLLM. A Comprehensive Framework for Benchmarking Fairness in Large Language Model Recommender Systems", + "abstract": "This paper presents a framework for evaluating fairness in recommender\nsystems powered by Large Language Models (RecLLMs), addressing the need for a\nunified approach that spans various fairness dimensions including sensitivity\nto user attributes, intrinsic fairness, and discussions of fairness based on\nunderlying benefits. In addition, our framework introduces counterfactual\nevaluations and integrates diverse user group considerations to enhance the\ndiscourse on fairness evaluation for RecLLMs.\n Our key contributions include the development of a robust framework for\nfairness evaluation in LLM-based recommendations and a structured method to\ncreate \\textit{informative user profiles} from demographic data, historical\nuser preferences, and recent interactions. We argue that the latter is\nessential for enhancing personalization in such systems, especially in\ntemporal-driven scenarios. We demonstrate the utility of our framework through\npractical applications on two datasets, LastFM-1K and ML-1M. We conduct\nexperiments on a subsample of 80 users from each dataset, testing and assessing\nthe effectiveness of various prompt construction scenarios and in-context\nlearning, comprising more than 50 scenarios. This results in more than 4000\nrecommendations (80 * 50 = 4000). Our study reveals that while there are no\nsignificant unfairness issues in scenarios involving sensitive attributes, some\nconcerns remain. However, in terms of intrinsic fairness, which does not\ninvolve direct sensitivity, unfairness across demographic groups remains\nsignificant. The code and data used for this paper are available at:\n\\url{https://shorturl.at/awBFM}.", + "authors": "Yashar Deldjoo", + "published": "2024-05-03", + "updated": "2024-05-03", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.09219v5", + "title": "\"Kelly is a Warm Person, Joseph is a Role Model\": Gender Biases in LLM-Generated Reference Letters", + "abstract": "Large Language Models (LLMs) have recently emerged as an effective tool to\nassist individuals in writing various types of content, including professional\ndocuments such as recommendation letters. Though bringing convenience, this\napplication also introduces unprecedented fairness concerns. Model-generated\nreference letters might be directly used by users in professional scenarios. If\nunderlying biases exist in these model-constructed letters, using them without\nscrutinization could lead to direct societal harms, such as sabotaging\napplication success rates for female applicants. In light of this pressing\nissue, it is imminent and necessary to comprehensively study fairness issues\nand associated harms in this real-world use case. In this paper, we critically\nexamine gender biases in LLM-generated reference letters. Drawing inspiration\nfrom social science findings, we design evaluation methods to manifest biases\nthrough 2 dimensions: (1) biases in language style and (2) biases in lexical\ncontent. We further investigate the extent of bias propagation by analyzing the\nhallucination bias of models, a term that we define to be bias exacerbation in\nmodel-hallucinated contents. Through benchmarking evaluation on 2 popular LLMs-\nChatGPT and Alpaca, we reveal significant gender biases in LLM-generated\nrecommendation letters. Our findings not only warn against using LLMs for this\napplication without scrutinization, but also illuminate the importance of\nthoroughly studying hidden biases and harms in LLM-generated professional\ndocuments.", + "authors": "Yixin Wan, George Pu, Jiao Sun, Aparna Garimella, Kai-Wei Chang, Nanyun Peng", + "published": "2023-10-13", + "updated": "2023-12-01", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2308.10149v2", + "title": "A Survey on Fairness in Large Language Models", + "abstract": "Large Language Models (LLMs) have shown powerful performance and development\nprospects and are widely deployed in the real world. However, LLMs can capture\nsocial biases from unprocessed training data and propagate the biases to\ndownstream tasks. Unfair LLM systems have undesirable social impacts and\npotential harms. In this paper, we provide a comprehensive review of related\nresearch on fairness in LLMs. Considering the influence of parameter magnitude\nand training paradigm on research strategy, we divide existing fairness\nresearch into oriented to medium-sized LLMs under pre-training and fine-tuning\nparadigms and oriented to large-sized LLMs under prompting paradigms. First,\nfor medium-sized LLMs, we introduce evaluation metrics and debiasing methods\nfrom the perspectives of intrinsic bias and extrinsic bias, respectively. Then,\nfor large-sized LLMs, we introduce recent fairness research, including fairness\nevaluation, reasons for bias, and debiasing methods. Finally, we discuss and\nprovide insight on the challenges and future directions for the development of\nfairness in LLMs.", + "authors": "Yingji Li, Mengnan Du, Rui Song, Xin Wang, Ying Wang", + "published": "2023-08-20", + "updated": "2024-02-21", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.17916v2", + "title": "LLM-Resistant Math Word Problem Generation via Adversarial Attacks", + "abstract": "Large language models (LLMs) have significantly transformed the educational\nlandscape. As current plagiarism detection tools struggle to keep pace with\nLLMs' rapid advancements, the educational community faces the challenge of\nassessing students' true problem-solving abilities in the presence of LLMs. In\nthis work, we explore a new paradigm for ensuring fair evaluation -- generating\nadversarial examples which preserve the structure and difficulty of the\noriginal questions aimed for assessment, but are unsolvable by LLMs. Focusing\non the domain of math word problems, we leverage abstract syntax trees to\nstructurally generate adversarial examples that cause LLMs to produce incorrect\nanswers by simply editing the numeric values in the problems. We conduct\nexperiments on various open- and closed-source LLMs, quantitatively and\nqualitatively demonstrating that our method significantly degrades their math\nproblem-solving ability. We identify shared vulnerabilities among LLMs and\npropose a cost-effective approach to attack high-cost models. Additionally, we\nconduct automatic analysis on math problems and investigate the cause of\nfailure, offering a nuanced view into model's limitation.", + "authors": "Roy Xie, Chengxuan Huang, Junlin Wang, Bhuwan Dhingra", + "published": "2024-02-27", + "updated": "2024-03-30", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.18130v2", + "title": "DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues", + "abstract": "Controversy is a reflection of our zeitgeist, and an important aspect to any\ndiscourse. The rise of large language models (LLMs) as conversational systems\nhas increased public reliance on these systems for answers to their various\nquestions. Consequently, it is crucial to systematically examine how these\nmodels respond to questions that pertaining to ongoing debates. However, few\nsuch datasets exist in providing human-annotated labels reflecting the\ncontemporary discussions. To foster research in this area, we propose a novel\nconstruction of a controversial questions dataset, expanding upon the publicly\nreleased Quora Question Pairs Dataset. This dataset presents challenges\nconcerning knowledge recency, safety, fairness, and bias. We evaluate different\nLLMs using a subset of this dataset, illuminating how they handle controversial\nissues and the stances they adopt. This research ultimately contributes to our\nunderstanding of LLMs' interaction with controversial issues, paving the way\nfor improvements in their comprehension and handling of complex societal\ndebates.", + "authors": "David Q. Sun, Artem Abzaliev, Hadas Kotek, Zidi Xiu, Christopher Klein, Jason D. Williams", + "published": "2023-10-27", + "updated": "2023-11-07", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.HC" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2307.11761v1", + "title": "Fairness of ChatGPT and the Role Of Explainable-Guided Prompts", + "abstract": "Our research investigates the potential of Large-scale Language Models\n(LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary\nclassification task. Our findings suggest that LLMs, when directed by\njudiciously designed prompts and supplemented with domain-specific knowledge,\ncan parallel the performance of traditional Machine Learning (ML) models.\nIntriguingly, they achieve this with significantly less data-40 times less,\nutilizing merely 20 data points compared to the ML's 800. LLMs particularly\nexcel in minimizing false positives and enhancing fairness, both being vital\naspects of risk analysis. While our results did not surpass those of classical\nML models, they underscore the potential of LLMs in analogous tasks, laying a\ngroundwork for future explorations into harnessing the capabilities of LLMs in\ndiverse ML tasks.", + "authors": "Yashar Deldjoo", + "published": "2023-07-14", + "updated": "2023-07-14", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.12736v1", + "title": "Large Language Model Supply Chain: A Research Agenda", + "abstract": "The rapid advancements in pre-trained Large Language Models (LLMs) and Large\nMultimodal Models (LMMs) have ushered in a new era of intelligent applications,\ntransforming fields ranging from natural language processing to content\ngeneration. The LLM supply chain represents a crucial aspect of the\ncontemporary artificial intelligence landscape. It encompasses the entire\nlifecycle of pre-trained models, from its initial development and training to\nits final deployment and application in various domains. This paper presents a\ncomprehensive overview of the LLM supply chain, highlighting its three core\nelements: 1) the model infrastructure, encompassing datasets and toolchain for\ntraining, optimization, and deployment; 2) the model lifecycle, covering\ntraining, testing, releasing, and ongoing maintenance; and 3) the downstream\napplication ecosystem, enabling the integration of pre-trained models into a\nwide range of intelligent applications. However, this rapidly evolving field\nfaces numerous challenges across these key components, including data privacy\nand security, model interpretability and fairness, infrastructure scalability,\nand regulatory compliance. Addressing these challenges is essential for\nharnessing the full potential of LLMs and ensuring their ethical and\nresponsible use. This paper provides a future research agenda for the LLM\nsupply chain, aiming at driving the continued advancement and responsible\ndeployment of these transformative LLMs.", + "authors": "Shenao Wang, Yanjie Zhao, Xinyi Hou, Haoyu Wang", + "published": "2024-04-19", + "updated": "2024-04-19", + "primary_cat": "cs.SE", + "cats": [ + "cs.SE" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.13343v1", + "title": "Challenges and Contributing Factors in the Utilization of Large Language Models (LLMs)", + "abstract": "With the development of large language models (LLMs) like the GPT series,\ntheir widespread use across various application scenarios presents a myriad of\nchallenges. This review initially explores the issue of domain specificity,\nwhere LLMs may struggle to provide precise answers to specialized questions\nwithin niche fields. The problem of knowledge forgetting arises as these LLMs\nmight find it hard to balance old and new information. The knowledge repetition\nphenomenon reveals that sometimes LLMs might deliver overly mechanized\nresponses, lacking depth and originality. Furthermore, knowledge illusion\ndescribes situations where LLMs might provide answers that seem insightful but\nare actually superficial, while knowledge toxicity focuses on harmful or biased\ninformation outputs. These challenges underscore problems in the training data\nand algorithmic design of LLMs. To address these issues, it's suggested to\ndiversify training data, fine-tune models, enhance transparency and\ninterpretability, and incorporate ethics and fairness training. Future\ntechnological trends might lean towards iterative methodologies, multimodal\nlearning, model personalization and customization, and real-time learning and\nfeedback mechanisms. In conclusion, future LLMs should prioritize fairness,\ntransparency, and ethics, ensuring they uphold high moral and ethical standards\nwhen serving humanity.", + "authors": "Xiaoliang Chen, Liangbin Li, Le Chang, Yunhe Huang, Yuxuan Zhao, Yuxiao Zhang, Dinuo Li", + "published": "2023-10-20", + "updated": "2023-10-20", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2405.01769v1", + "title": "A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law", + "abstract": "In the fast-evolving domain of artificial intelligence, large language models\n(LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance,\nhealthcare, and law: domains characterized by their reliance on professional\nexpertise, challenging data acquisition, high-stakes, and stringent regulatory\ncompliance. This survey offers a detailed exploration of the methodologies,\napplications, challenges, and forward-looking opportunities of LLMs within\nthese high-stakes sectors. We highlight the instrumental role of LLMs in\nenhancing diagnostic and treatment methodologies in healthcare, innovating\nfinancial analytics, and refining legal interpretation and compliance\nstrategies. Moreover, we critically examine the ethics for LLM applications in\nthese fields, pointing out the existing ethical concerns and the need for\ntransparent, fair, and robust AI systems that respect regulatory norms. By\npresenting a thorough review of current literature and practical applications,\nwe showcase the transformative impact of LLMs, and outline the imperative for\ninterdisciplinary cooperation, methodological advancements, and ethical\nvigilance. Through this lens, we aim to spark dialogue and inspire future\nresearch dedicated to maximizing the benefits of LLMs while mitigating their\nrisks in these precision-dependent sectors. To facilitate future research on\nLLMs in these critical societal domains, we also initiate a reading list that\ntracks the latest advancements under this topic, which will be continually\nupdated: \\url{https://github.com/czyssrs/LLM_X_papers}.", + "authors": "Zhiyu Zoey Chen, Jing Ma, Xinlu Zhang, Nan Hao, An Yan, Armineh Nourbakhsh, Xianjun Yang, Julian McAuley, Linda Petzold, William Yang Wang", + "published": "2024-05-02", + "updated": "2024-05-02", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.18502v1", + "title": "Few-Shot Fairness: Unveiling LLM's Potential for Fairness-Aware Classification", + "abstract": "Employing Large Language Models (LLM) in various downstream applications such\nas classification is crucial, especially for smaller companies lacking the\nexpertise and resources required for fine-tuning a model. Fairness in LLMs\nhelps ensure inclusivity, equal representation based on factors such as race,\ngender and promotes responsible AI deployment. As the use of LLMs has become\nincreasingly prevalent, it is essential to assess whether LLMs can generate\nfair outcomes when subjected to considerations of fairness. In this study, we\nintroduce a framework outlining fairness regulations aligned with various\nfairness definitions, with each definition being modulated by varying degrees\nof abstraction. We explore the configuration for in-context learning and the\nprocedure for selecting in-context demonstrations using RAG, while\nincorporating fairness rules into the process. Experiments conducted with\ndifferent LLMs indicate that GPT-4 delivers superior results in terms of both\naccuracy and fairness compared to other models. This work is one of the early\nattempts to achieve fairness in prediction tasks by utilizing LLMs through\nin-context learning.", + "authors": "Garima Chhikara, Anurag Sharma, Kripabandhu Ghosh, Abhijnan Chakraborty", + "published": "2024-02-28", + "updated": "2024-02-28", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.14607v2", + "title": "Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications", + "abstract": "Recent literature has suggested the potential of using large language models\n(LLMs) to make classifications for tabular tasks. However, LLMs have been shown\nto exhibit harmful social biases that reflect the stereotypes and inequalities\npresent in society. To this end, as well as the widespread use of tabular data\nin many high-stake applications, it is important to explore the following\nquestions: what sources of information do LLMs draw upon when making\nclassifications for tabular tasks; whether and to what extent are LLM\nclassifications for tabular data influenced by social biases and stereotypes;\nand what are the consequential implications for fairness?\n Through a series of experiments, we delve into these questions and show that\nLLMs tend to inherit social biases from their training data which significantly\nimpact their fairness in tabular classification tasks. Furthermore, our\ninvestigations show that in the context of bias mitigation, though in-context\nlearning and finetuning have a moderate effect, the fairness metric gap between\ndifferent subgroups is still larger than that in traditional machine learning\nmodels, such as Random Forest and shallow Neural Networks. This observation\nemphasizes that the social biases are inherent within the LLMs themselves and\ninherited from their pretraining corpus, not only from the downstream task\ndatasets. Besides, we demonstrate that label-flipping of in-context examples\ncan significantly reduce biases, further highlighting the presence of inherent\nbias within LLMs.", + "authors": "Yanchen Liu, Srishti Gautam, Jiaqi Ma, Himabindu Lakkaraju", + "published": "2023-10-23", + "updated": "2024-04-02", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.07609v3", + "title": "Is ChatGPT Fair for Recommendation? Evaluating Fairness in Large Language Model Recommendation", + "abstract": "The remarkable achievements of Large Language Models (LLMs) have led to the\nemergence of a novel recommendation paradigm -- Recommendation via LLM\n(RecLLM). Nevertheless, it is important to note that LLMs may contain social\nprejudices, and therefore, the fairness of recommendations made by RecLLM\nrequires further investigation. To avoid the potential risks of RecLLM, it is\nimperative to evaluate the fairness of RecLLM with respect to various sensitive\nattributes on the user side. Due to the differences between the RecLLM paradigm\nand the traditional recommendation paradigm, it is problematic to directly use\nthe fairness benchmark of traditional recommendation. To address the dilemma,\nwe propose a novel benchmark called Fairness of Recommendation via LLM\n(FaiRLLM). This benchmark comprises carefully crafted metrics and a dataset\nthat accounts for eight sensitive attributes1 in two recommendation scenarios:\nmusic and movies. By utilizing our FaiRLLM benchmark, we conducted an\nevaluation of ChatGPT and discovered that it still exhibits unfairness to some\nsensitive attributes when generating recommendations. Our code and dataset can\nbe found at https://github.com/jizhi-zhang/FaiRLLM.", + "authors": "Jizhi Zhang, Keqin Bao, Yang Zhang, Wenjie Wang, Fuli Feng, Xiangnan He", + "published": "2023-05-12", + "updated": "2023-10-17", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR", + "cs.CL", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.15007v1", + "title": "Did the Neurons Read your Book? Document-level Membership Inference for Large Language Models", + "abstract": "With large language models (LLMs) poised to become embedded in our daily\nlives, questions are starting to be raised about the dataset(s) they learned\nfrom. These questions range from potential bias or misinformation LLMs could\nretain from their training data to questions of copyright and fair use of\nhuman-generated text. However, while these questions emerge, developers of the\nrecent state-of-the-art LLMs become increasingly reluctant to disclose details\non their training corpus. We here introduce the task of document-level\nmembership inference for real-world LLMs, i.e. inferring whether the LLM has\nseen a given document during training or not. First, we propose a procedure for\nthe development and evaluation of document-level membership inference for LLMs\nby leveraging commonly used data sources for training and the model release\ndate. We then propose a practical, black-box method to predict document-level\nmembership and instantiate it on OpenLLaMA-7B with both books and academic\npapers. We show our methodology to perform very well, reaching an impressive\nAUC of 0.856 for books and 0.678 for papers. We then show our approach to\noutperform the sentence-level membership inference attacks used in the privacy\nliterature for the document-level membership task. We finally evaluate whether\nsmaller models might be less sensitive to document-level inference and show\nOpenLLaMA-3B to be approximately as sensitive as OpenLLaMA-7B to our approach.\nTaken together, our results show that accurate document-level membership can be\ninferred for LLMs, increasing the transparency of technology poised to change\nour lives.", + "authors": "Matthieu Meeus, Shubham Jain, Marek Rei, Yves-Alexandre de Montjoye", + "published": "2023-10-23", + "updated": "2023-10-23", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.CR", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.18276v1", + "title": "Bias Neutralization Framework: Measuring Fairness in Large Language Models with Bias Intelligence Quotient (BiQ)", + "abstract": "The burgeoning influence of Large Language Models (LLMs) in shaping public\ndiscourse and decision-making underscores the imperative to address inherent\nbiases within these AI systems. In the wake of AI's expansive integration\nacross sectors, addressing racial bias in LLMs has never been more critical.\nThis paper introduces a novel framework called Comprehensive Bias\nNeutralization Framework (CBNF) which embodies an innovative approach to\nquantifying and mitigating biases within LLMs. Our framework combines the Large\nLanguage Model Bias Index (LLMBI) [Oketunji, A., Anas, M., Saina, D., (2023)]\nand Bias removaL with No Demographics (BLIND) [Orgad, H., Belinkov, Y. (2023)]\nmethodologies to create a new metric called Bias Intelligence Quotient\n(BiQ)which detects, measures, and mitigates racial bias in LLMs without\nreliance on demographic annotations.\n By introducing a new metric called BiQ that enhances LLMBI with additional\nfairness metrics, CBNF offers a multi-dimensional metric for bias assessment,\nunderscoring the necessity of a nuanced approach to fairness in AI [Mehrabi et\nal., 2021]. This paper presents a detailed analysis of Latimer AI (a language\nmodel incrementally trained on black history and culture) in comparison to\nChatGPT 3.5, illustrating Latimer AI's efficacy in detecting racial, cultural,\nand gender biases through targeted training and refined bias mitigation\nstrategies [Latimer & Bender, 2023].", + "authors": "Malur Narayan, John Pasmore, Elton Sampaio, Vijay Raghavan, Gabriella Waters", + "published": "2024-04-28", + "updated": "2024-04-28", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "D.1; I.2" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.02680v1", + "title": "Large Language Models are Geographically Biased", + "abstract": "Large Language Models (LLMs) inherently carry the biases contained in their\ntraining corpora, which can lead to the perpetuation of societal harm. As the\nimpact of these foundation models grows, understanding and evaluating their\nbiases becomes crucial to achieving fairness and accuracy. We propose to study\nwhat LLMs know about the world we live in through the lens of geography. This\napproach is particularly powerful as there is ground truth for the numerous\naspects of human life that are meaningfully projected onto geographic space\nsuch as culture, race, language, politics, and religion. We show various\nproblematic geographic biases, which we define as systemic errors in geospatial\npredictions. Initially, we demonstrate that LLMs are capable of making accurate\nzero-shot geospatial predictions in the form of ratings that show strong\nmonotonic correlation with ground truth (Spearman's $\\rho$ of up to 0.89). We\nthen show that LLMs exhibit common biases across a range of objective and\nsubjective topics. In particular, LLMs are clearly biased against locations\nwith lower socioeconomic conditions (e.g. most of Africa) on a variety of\nsensitive subjective topics such as attractiveness, morality, and intelligence\n(Spearman's $\\rho$ of up to 0.70). Finally, we introduce a bias score to\nquantify this and find that there is significant variation in the magnitude of\nbias across existing LLMs.", + "authors": "Rohin Manvi, Samar Khanna, Marshall Burke, David Lobell, Stefano Ermon", + "published": "2024-02-05", + "updated": "2024-02-05", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.05694v1", + "title": "A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics", + "abstract": "The utilization of large language models (LLMs) in the Healthcare domain has\ngenerated both excitement and concern due to their ability to effectively\nrespond to freetext queries with certain professional knowledge. This survey\noutlines the capabilities of the currently developed LLMs for Healthcare and\nexplicates their development process, with the aim of providing an overview of\nthe development roadmap from traditional Pretrained Language Models (PLMs) to\nLLMs. Specifically, we first explore the potential of LLMs to enhance the\nefficiency and effectiveness of various Healthcare applications highlighting\nboth the strengths and limitations. Secondly, we conduct a comparison between\nthe previous PLMs and the latest LLMs, as well as comparing various LLMs with\neach other. Then we summarize related Healthcare training data, training\nmethods, optimization strategies, and usage. Finally, the unique concerns\nassociated with deploying LLMs in Healthcare settings are investigated,\nparticularly regarding fairness, accountability, transparency and ethics. Our\nsurvey provide a comprehensive investigation from perspectives of both computer\nscience and Healthcare specialty. Besides the discussion about Healthcare\nconcerns, we supports the computer science community by compiling a collection\nof open source resources, such as accessible datasets, the latest\nmethodologies, code implementations, and evaluation benchmarks in the Github.\nSummarily, we contend that a significant paradigm shift is underway,\ntransitioning from PLMs to LLMs. This shift encompasses a move from\ndiscriminative AI approaches to generative AI approaches, as well as a shift\nfrom model-centered methodologies to datacentered methodologies.", + "authors": "Kai He, Rui Mao, Qika Lin, Yucheng Ruan, Xiang Lan, Mengling Feng, Erik Cambria", + "published": "2023-10-09", + "updated": "2023-10-09", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.00811v1", + "title": "Cognitive Bias in High-Stakes Decision-Making with LLMs", + "abstract": "Large language models (LLMs) offer significant potential as tools to support\nan expanding range of decision-making tasks. However, given their training on\nhuman (created) data, LLMs can inherit both societal biases against protected\ngroups, as well as be subject to cognitive bias. Such human-like bias can\nimpede fair and explainable decisions made with LLM assistance. Our work\nintroduces BiasBuster, a framework designed to uncover, evaluate, and mitigate\ncognitive bias in LLMs, particularly in high-stakes decision-making tasks.\nInspired by prior research in psychology and cognitive sciences, we develop a\ndataset containing 16,800 prompts to evaluate different cognitive biases (e.g.,\nprompt-induced, sequential, inherent). We test various bias mitigation\nstrategies, amidst proposing a novel method using LLMs to debias their own\nprompts. Our analysis provides a comprehensive picture on the presence and\neffects of cognitive bias across different commercial and open-source models.\nWe demonstrate that our self-help debiasing effectively mitigate cognitive bias\nwithout having to manually craft examples for each bias type.", + "authors": "Jessica Echterhoff, Yao Liu, Abeer Alessa, Julian McAuley, Zexue He", + "published": "2024-02-25", + "updated": "2024-02-25", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.06003v1", + "title": "FreeEval: A Modular Framework for Trustworthy and Efficient Evaluation of Large Language Models", + "abstract": "The rapid development of large language model (LLM) evaluation methodologies\nand datasets has led to a profound challenge: integrating state-of-the-art\nevaluation techniques cost-effectively while ensuring reliability,\nreproducibility, and efficiency. Currently, there is a notable absence of a\nunified and adaptable framework that seamlessly integrates various evaluation\napproaches. Moreover, the reliability of evaluation findings is often\nquestionable due to potential data contamination, with the evaluation\nefficiency commonly overlooked when facing the substantial costs associated\nwith LLM inference. In response to these challenges, we introduce FreeEval, a\nmodular and scalable framework crafted to enable trustworthy and efficient\nautomatic evaluations of LLMs. Firstly, FreeEval's unified abstractions\nsimplify the integration and improve the transparency of diverse evaluation\nmethodologies, encompassing dynamic evaluation that demand sophisticated LLM\ninteractions. Secondly, the framework integrates meta-evaluation techniques\nlike human evaluation and data contamination detection, which, along with\ndynamic evaluation modules in the platform, enhance the fairness of the\nevaluation outcomes. Lastly, FreeEval is designed with a high-performance\ninfrastructure, including distributed computation and caching strategies,\nenabling extensive evaluations across multi-node, multi-GPU clusters for\nopen-source and proprietary LLMs.", + "authors": "Zhuohao Yu, Chang Gao, Wenjin Yao, Yidong Wang, Zhengran Zeng, Wei Ye, Jindong Wang, Yue Zhang, Shikun Zhang", + "published": "2024-04-09", + "updated": "2024-04-09", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.01349v1", + "title": "Fairness in Large Language Models: A Taxonomic Survey", + "abstract": "Large Language Models (LLMs) have demonstrated remarkable success across\nvarious domains. However, despite their promising performance in numerous\nreal-world applications, most of these algorithms lack fairness considerations.\nConsequently, they may lead to discriminatory outcomes against certain\ncommunities, particularly marginalized populations, prompting extensive study\nin fair LLMs. On the other hand, fairness in LLMs, in contrast to fairness in\ntraditional machine learning, entails exclusive backgrounds, taxonomies, and\nfulfillment techniques. To this end, this survey presents a comprehensive\noverview of recent advances in the existing literature concerning fair LLMs.\nSpecifically, a brief introduction to LLMs is provided, followed by an analysis\nof factors contributing to bias in LLMs. Additionally, the concept of fairness\nin LLMs is discussed categorically, summarizing metrics for evaluating bias in\nLLMs and existing algorithms for promoting fairness. Furthermore, resources for\nevaluating bias in LLMs, including toolkits and datasets, are summarized.\nFinally, existing research challenges and open questions are discussed.", + "authors": "Zhibo Chu, Zichong Wang, Wenbin Zhang", + "published": "2024-03-31", + "updated": "2024-03-31", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.08780v1", + "title": "\"Im not Racist but...\": Discovering Bias in the Internal Knowledge of Large Language Models", + "abstract": "Large language models (LLMs) have garnered significant attention for their\nremarkable performance in a continuously expanding set of natural language\nprocessing tasks. However, these models have been shown to harbor inherent\nsocietal biases, or stereotypes, which can adversely affect their performance\nin their many downstream applications. In this paper, we introduce a novel,\npurely prompt-based approach to uncover hidden stereotypes within any arbitrary\nLLM. Our approach dynamically generates a knowledge representation of internal\nstereotypes, enabling the identification of biases encoded within the LLM's\ninternal knowledge. By illuminating the biases present in LLMs and offering a\nsystematic methodology for their analysis, our work contributes to advancing\ntransparency and promoting fairness in natural language processing systems.", + "authors": "Abel Salinas, Louis Penafiel, Robert McCormack, Fred Morstatter", + "published": "2023-10-13", + "updated": "2023-10-13", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.01964v1", + "title": "Don't Make Your LLM an Evaluation Benchmark Cheater", + "abstract": "Large language models~(LLMs) have greatly advanced the frontiers of\nartificial intelligence, attaining remarkable improvement in model capacity. To\nassess the model performance, a typical approach is to construct evaluation\nbenchmarks for measuring the ability level of LLMs in different aspects.\nDespite that a number of high-quality benchmarks have been released, the\nconcerns about the appropriate use of these benchmarks and the fair comparison\nof different models are increasingly growing. Considering these concerns, in\nthis paper, we discuss the potential risk and impact of inappropriately using\nevaluation benchmarks and misleadingly interpreting the evaluation results.\nSpecially, we focus on a special issue that would lead to inappropriate\nevaluation, \\ie \\emph{benchmark leakage}, referring that the data related to\nevaluation sets is occasionally used for model training. This phenomenon now\nbecomes more common since pre-training data is often prepared ahead of model\ntest. We conduct extensive experiments to study the effect of benchmark\nleverage, and find that it can dramatically boost the evaluation results, which\nwould finally lead to an unreliable assessment of model performance. To improve\nthe use of existing evaluation benchmarks, we finally present several\nguidelines for both LLM developers and benchmark maintainers. We hope this work\ncan draw attention to appropriate training and evaluation of LLMs.", + "authors": "Kun Zhou, Yutao Zhu, Zhipeng Chen, Wentong Chen, Wayne Xin Zhao, Xu Chen, Yankai Lin, Ji-Rong Wen, Jiawei Han", + "published": "2023-11-03", + "updated": "2023-11-03", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2310.16343v2", + "title": "Evaluating, Understanding, and Improving Constrained Text Generation for Large Language Models", + "abstract": "Advancements in natural language generation (NLG) and large language models\n(LLMs) have led to proficient text generation in various tasks. However,\nintegrating intricate constraints into neural text generation, due to LLMs'\nopacity, remains challenging. This study investigates constrained text\ngeneration for LLMs, where predefined constraints are applied during LLM's\ngeneration process. Our research mainly focuses on mainstream open-source LLMs,\ncategorizing constraints into lexical, structural, and relation-based types. We\nalso present various benchmarks to facilitate fair evaluation. The study\naddresses some key research questions, including evaluating, understanding and\nimproving constrained text generation for LLMs. Results illuminate LLMs'\ncapacity and deficiency to incorporate constraints and provide insights for\nfuture developments in constrained text generation. Codes and datasets will be\nreleased upon acceptance.", + "authors": "Xiang Chen, Xiaojun Wan", + "published": "2023-10-25", + "updated": "2024-03-21", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.06852v2", + "title": "ChemLLM: A Chemical Large Language Model", + "abstract": "Large language models (LLMs) have made impressive progress in chemistry\napplications. However, the community lacks an LLM specifically designed for\nchemistry. The main challenges are two-fold: firstly, most chemical data and\nscientific knowledge are stored in structured databases, which limits the\nmodel's ability to sustain coherent dialogue when used directly. Secondly,\nthere is an absence of objective and fair benchmark that encompass most\nchemistry tasks. Here, we introduce ChemLLM, a comprehensive framework that\nfeatures the first LLM dedicated to chemistry. It also includes ChemData, a\ndataset specifically designed for instruction tuning, and ChemBench, a robust\nbenchmark covering nine essential chemistry tasks. ChemLLM is adept at\nperforming various tasks across chemical disciplines with fluid dialogue\ninteraction. Notably, ChemLLM achieves results comparable to GPT-4 on the core\nchemical tasks and demonstrates competitive performance with LLMs of similar\nsize in general scenarios. ChemLLM paves a new path for exploration in chemical\nstudies, and our method of incorporating structured chemical knowledge into\ndialogue systems sets a new standard for developing LLMs in various scientific\nfields. Codes, Datasets, and Model weights are publicly accessible at\nhttps://hf.co/AI4Chem", + "authors": "Di Zhang, Wei Liu, Qian Tan, Jingdan Chen, Hang Yan, Yuliang Yan, Jiatong Li, Weiran Huang, Xiangyu Yue, Wanli Ouyang, Dongzhan Zhou, Shufei Zhang, Mao Su, Han-Sen Zhong, Yuqiang Li", + "published": "2024-02-10", + "updated": "2024-04-25", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.11764v1", + "title": "ChatGPT Based Data Augmentation for Improved Parameter-Efficient Debiasing of LLMs", + "abstract": "Large Language models (LLMs), while powerful, exhibit harmful social biases.\nDebiasing is often challenging due to computational costs, data constraints,\nand potential degradation of multi-task language capabilities. This work\nintroduces a novel approach utilizing ChatGPT to generate synthetic training\ndata, aiming to enhance the debiasing of LLMs. We propose two strategies:\nTargeted Prompting, which provides effective debiasing for known biases but\nnecessitates prior specification of bias in question; and General Prompting,\nwhich, while slightly less effective, offers debiasing across various\ncategories. We leverage resource-efficient LLM debiasing using adapter tuning\nand compare the effectiveness of our synthetic data to existing debiasing\ndatasets. Our results reveal that: (1) ChatGPT can efficiently produce\nhigh-quality training data for debiasing other LLMs; (2) data produced via our\napproach surpasses existing datasets in debiasing performance while also\npreserving internal knowledge of a pre-trained LLM; and (3) synthetic data\nexhibits generalizability across categories, effectively mitigating various\nbiases, including intersectional ones. These findings underscore the potential\nof synthetic data in advancing the fairness of LLMs with minimal retraining\ncost.", + "authors": "Pengrui Han, Rafal Kocielnik, Adhithya Saravanan, Roy Jiang, Or Sharir, Anima Anandkumar", + "published": "2024-02-19", + "updated": "2024-02-19", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY", + "68T50", + "I.2.7; K.4.1" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.11406v2", + "title": "Don't Go To Extremes: Revealing the Excessive Sensitivity and Calibration Limitations of LLMs in Implicit Hate Speech Detection", + "abstract": "The fairness and trustworthiness of Large Language Models (LLMs) are\nreceiving increasing attention. Implicit hate speech, which employs indirect\nlanguage to convey hateful intentions, occupies a significant portion of\npractice. However, the extent to which LLMs effectively address this issue\nremains insufficiently examined. This paper delves into the capability of LLMs\nto detect implicit hate speech (Classification Task) and express confidence in\ntheir responses (Calibration Task). Our evaluation meticulously considers\nvarious prompt patterns and mainstream uncertainty estimation methods. Our\nfindings highlight that LLMs exhibit two extremes: (1) LLMs display excessive\nsensitivity towards groups or topics that may cause fairness issues, resulting\nin misclassifying benign statements as hate speech. (2) LLMs' confidence scores\nfor each method excessively concentrate on a fixed range, remaining unchanged\nregardless of the dataset's complexity. Consequently, the calibration\nperformance is heavily reliant on primary classification accuracy. These\ndiscoveries unveil new limitations of LLMs, underscoring the need for caution\nwhen optimizing models to ensure they do not veer towards extremes. This serves\nas a reminder to carefully consider sensitivity and confidence in the pursuit\nof model fairness.", + "authors": "Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu", + "published": "2024-02-18", + "updated": "2024-02-26", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2401.11033v4", + "title": "FAIR Enough: How Can We Develop and Assess a FAIR-Compliant Dataset for Large Language Models' Training?", + "abstract": "The rapid evolution of Large Language Models (LLMs) highlights the necessity\nfor ethical considerations and data integrity in AI development, particularly\nemphasizing the role of FAIR (Findable, Accessible, Interoperable, Reusable)\ndata principles. While these principles are crucial for ethical data\nstewardship, their specific application in the context of LLM training data\nremains an under-explored area. This research gap is the focus of our study,\nwhich begins with an examination of existing literature to underline the\nimportance of FAIR principles in managing data for LLM training. Building upon\nthis, we propose a novel framework designed to integrate FAIR principles into\nthe LLM development lifecycle. A contribution of our work is the development of\na comprehensive checklist intended to guide researchers and developers in\napplying FAIR data principles consistently across the model development\nprocess. The utility and effectiveness of our framework are validated through a\ncase study on creating a FAIR-compliant dataset aimed at detecting and\nmitigating biases in LLMs. We present this framework to the community as a tool\nto foster the creation of technologically advanced, ethically grounded, and\nsocially responsible AI models.", + "authors": "Shaina Raza, Shardul Ghuge, Chen Ding, Elham Dolatabadi, Deval Pandya", + "published": "2024-01-19", + "updated": "2024-04-03", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.13925v1", + "title": "MARIO Eval: Evaluate Your Math LLM with your Math LLM--A mathematical dataset evaluation toolkit", + "abstract": "Large language models (LLMs) have been explored in a variety of reasoning\ntasks including solving of mathematical problems. Each math dataset typically\nincludes its own specially designed evaluation script, which, while suitable\nfor its intended use, lacks generalizability across different datasets.\nConsequently, updates and adaptations to these evaluation tools tend to occur\nwithout being systematically reported, leading to inconsistencies and obstacles\nto fair comparison across studies. To bridge this gap, we introduce a\ncomprehensive mathematical evaluation toolkit that not only utilizes a python\ncomputer algebra system (CAS) for its numerical accuracy, but also integrates\nan optional LLM, known for its considerable natural language processing\ncapabilities. To validate the effectiveness of our toolkit, we manually\nannotated two distinct datasets. Our experiments demonstrate that the toolkit\nyields more robust evaluation results compared to prior works, even without an\nLLM. Furthermore, when an LLM is incorporated, there is a notable enhancement.\nThe code for our method will be made available at\n\\url{https://github.com/MARIO-Math-Reasoning/math_evaluation}.", + "authors": "Boning Zhang, Chengxi Li, Kai Fan", + "published": "2024-04-22", + "updated": "2024-04-22", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2309.14345v2", + "title": "Bias Testing and Mitigation in LLM-based Code Generation", + "abstract": "Utilizing state-of-the-art Large Language Models (LLMs), automatic code\ngeneration models play a pivotal role in enhancing the productivity of software\ndevelopment procedures. As the adoption of LLMs becomes more widespread in\nsoftware coding ecosystems, a pressing issue has emerged: does the generated\ncode contain social bias and unfairness, such as those related to age, gender,\nand race? This issue concerns the integrity, fairness, and ethical foundation\nof software applications that depend on the code generated by these models, yet\nis under-explored in the literature. This paper presents a novel bias testing\nframework that is specifically designed for code generation tasks. Based on\nthis framework, we conduct an extensive evaluation of the bias in code\ngenerated by five state-of-the-art LLMs. Our findings reveal that 20.29% to\n44.93% code functions generated by the models under study are biased when\nhandling bias sensitive tasks (i.e., tasks that involve sensitive attributes\nsuch as age and gender). This indicates that the existing LLMs can be unfair in\ncode generation, posing risks of unintended and harmful software behaviors. To\nmitigate bias for code generation models, we evaluate five bias mitigation\nprompt strategies, i.e., utilizing bias testing results to refine the code\n(zero-shot), one-, few-shot, and two Chain-of-Thought (CoT) prompts. Our\nevaluation results illustrate that these strategies are all effective in\nmitigating bias. Overall, one-shot and few-shot learning are the two most\neffective. For GPT-4, 80% to 90% code bias can be removed with one-shot\nlearning.", + "authors": "Dong Huang, Qingwen Bu, Jie Zhang, Xiaofei Xie, Junjie Chen, Heming Cui", + "published": "2023-09-03", + "updated": "2024-01-09", + "primary_cat": "cs.SE", + "cats": [ + "cs.SE", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.19118v1", + "title": "Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate", + "abstract": "Modern large language models (LLMs) like ChatGPT have shown remarkable\nperformance on general language tasks but still struggle on complex reasoning\ntasks, which drives the research on cognitive behaviors of LLMs to explore\nhuman-like problem-solving strategies. Along this direction, one representative\nstrategy is self-reflection, which asks an LLM to refine the solution with the\nfeedback generated by itself iteratively. However, our study shows that such\nreflection-style methods suffer from the Degeneration-of-Thought (DoT) problem:\nonce the LLM has established confidence in its solutions, it is unable to\ngenerate novel thoughts later through reflection even if its initial stance is\nincorrect. To address the DoT problem, we propose a Multi-Agent Debate (MAD)\nframework, in which multiple agents express their arguments in the state of\n\"tit for tat\" and a judge manages the debate process to obtain a final\nsolution. Clearly, our MAD framework encourages divergent thinking in LLMs\nwhich would be helpful for tasks that require deep levels of contemplation.\nExperiment results on two challenging datasets, commonsense machine translation\nand counter-intuitive arithmetic reasoning, demonstrate the effectiveness of\nour MAD framework. Extensive analyses suggest that the adaptive break of debate\nand the modest level of \"tit for tat\" state are required for MAD to obtain good\nperformance. Moreover, we find that LLMs might not be a fair judge if different\nLLMs are used for agents. Codes:\nhttps://github.com/Skytliang/Multi-Agents-Debate", + "authors": "Tian Liang, Zhiwei He, Wenxiang Jiao, Xing Wang, Yan Wang, Rui Wang, Yujiu Yang, Zhaopeng Tu, Shuming Shi", + "published": "2023-05-30", + "updated": "2023-05-30", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.12150v1", + "title": "Your Large Language Model is Secretly a Fairness Proponent and You Should Prompt it Like One", + "abstract": "The widespread adoption of large language models (LLMs) underscores the\nurgent need to ensure their fairness. However, LLMs frequently present dominant\nviewpoints while ignoring alternative perspectives from minority parties,\nresulting in potential biases. We hypothesize that these fairness-violating\nbehaviors occur because LLMs express their viewpoints using a human personality\nthat represents the majority of training data. In response to this, we validate\nthat prompting LLMs with specific roles can allow LLMs to express diverse\nviewpoints. Building on this insight and observation, we develop FairThinking,\na pipeline designed to automatically generate roles that enable LLMs to\narticulate diverse perspectives for fair expressions. To evaluate FairThinking,\nwe create a dataset with a thousand items covering three fairness-related\ntopics and conduct experiments on GPT-3.5, GPT-4, Llama2, and Mistral to\ndemonstrate its superior performance.", + "authors": "Tianlin Li, Xiaoyu Zhang, Chao Du, Tianyu Pang, Qian Liu, Qing Guo, Chao Shen, Yang Liu", + "published": "2024-02-19", + "updated": "2024-02-19", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "I.2; J.4" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.14208v2", + "title": "Content Conditional Debiasing for Fair Text Embedding", + "abstract": "Mitigating biases in machine learning models has gained increasing attention\nin Natural Language Processing (NLP). Yet, only a few studies focus on fair\ntext embeddings, which are crucial yet challenging for real-world applications.\nIn this paper, we propose a novel method for learning fair text embeddings. We\nachieve fairness while maintaining utility trade-off by ensuring conditional\nindependence between sensitive attributes and text embeddings conditioned on\nthe content. Specifically, we enforce that embeddings of texts with different\nsensitive attributes but identical content maintain the same distance toward\nthe embedding of their corresponding neutral text. Furthermore, we address the\nissue of lacking proper training data by using Large Language Models (LLMs) to\naugment texts into different sensitive groups. Our extensive evaluations\ndemonstrate that our approach effectively improves fairness while preserving\nthe utility of embeddings, representing a pioneering effort in achieving\nconditional independence for fair text embeddings.", + "authors": "Wenlong Deng, Blair Chen, Xiaoxiao Li, Christos Thrampoulidis", + "published": "2024-02-22", + "updated": "2024-02-23", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.CY", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2311.04205v2", + "title": "Rephrase and Respond: Let Large Language Models Ask Better Questions for Themselves", + "abstract": "Misunderstandings arise not only in interpersonal communication but also\nbetween humans and Large Language Models (LLMs). Such discrepancies can make\nLLMs interpret seemingly unambiguous questions in unexpected ways, yielding\nincorrect responses. While it is widely acknowledged that the quality of a\nprompt, such as a question, significantly impacts the quality of the response\nprovided by LLMs, a systematic method for crafting questions that LLMs can\nbetter comprehend is still underdeveloped. In this paper, we present a method\nnamed `Rephrase and Respond' (RaR), which allows LLMs to rephrase and expand\nquestions posed by humans and provide responses in a single prompt. This\napproach serves as a simple yet effective prompting method for improving\nperformance. We also introduce a two-step variant of RaR, where a rephrasing\nLLM first rephrases the question and then passes the original and rephrased\nquestions together to a different responding LLM. This facilitates the\neffective utilization of rephrased questions generated by one LLM with another.\nOur experiments demonstrate that our methods significantly improve the\nperformance of different models across a wide range to tasks. We further\nprovide a comprehensive comparison between RaR and the popular Chain-of-Thought\n(CoT) methods, both theoretically and empirically. We show that RaR is\ncomplementary to CoT and can be combined with CoT to achieve even better\nperformance. Our work not only contributes to enhancing LLM performance\nefficiently and effectively but also sheds light on a fair evaluation of LLM\ncapabilities. Data and codes are available at\nhttps://github.com/uclaml/Rephrase-and-Respond.", + "authors": "Yihe Deng, Weitong Zhang, Zixiang Chen, Quanquan Gu", + "published": "2023-11-07", + "updated": "2024-04-18", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2403.05668v1", + "title": "CFaiRLLM: Consumer Fairness Evaluation in Large-Language Model Recommender System", + "abstract": "In the evolving landscape of recommender systems, the integration of Large\nLanguage Models (LLMs) such as ChatGPT marks a new era, introducing the concept\nof Recommendation via LLM (RecLLM). While these advancements promise\nunprecedented personalization and efficiency, they also bring to the fore\ncritical concerns regarding fairness, particularly in how recommendations might\ninadvertently perpetuate or amplify biases associated with sensitive user\nattributes. In order to address these concerns, our study introduces a\ncomprehensive evaluation framework, CFaiRLLM, aimed at evaluating (and thereby\nmitigating) biases on the consumer side within RecLLMs.\n Our research methodically assesses the fairness of RecLLMs by examining how\nrecommendations might vary with the inclusion of sensitive attributes such as\ngender, age, and their intersections, through both similarity alignment and\ntrue preference alignment. By analyzing recommendations generated under\ndifferent conditions-including the use of sensitive attributes in user\nprompts-our framework identifies potential biases in the recommendations\nprovided. A key part of our study involves exploring how different detailed\nstrategies for constructing user profiles (random, top-rated, recent) impact\nthe alignment between recommendations made without consideration of sensitive\nattributes and those that are sensitive-attribute-aware, highlighting the bias\nmechanisms within RecLLMs.\n The findings in our study highlight notable disparities in the fairness of\nrecommendations, particularly when sensitive attributes are integrated into the\nrecommendation process, either individually or in combination. The analysis\ndemonstrates that the choice of user profile sampling strategy plays a\nsignificant role in affecting fairness outcomes, highlighting the complexity of\nachieving fair recommendations in the era of LLMs.", + "authors": "Yashar Deldjoo, Tommaso di Noia", + "published": "2024-03-08", + "updated": "2024-03-08", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.03514v3", + "title": "Can Large Language Models Transform Computational Social Science?", + "abstract": "Large Language Models (LLMs) are capable of successfully performing many\nlanguage processing tasks zero-shot (without training data). If zero-shot LLMs\ncan also reliably classify and explain social phenomena like persuasiveness and\npolitical ideology, then LLMs could augment the Computational Social Science\n(CSS) pipeline in important ways. This work provides a road map for using LLMs\nas CSS tools. Towards this end, we contribute a set of prompting best practices\nand an extensive evaluation pipeline to measure the zero-shot performance of 13\nlanguage models on 25 representative English CSS benchmarks. On taxonomic\nlabeling tasks (classification), LLMs fail to outperform the best fine-tuned\nmodels but still achieve fair levels of agreement with humans. On free-form\ncoding tasks (generation), LLMs produce explanations that often exceed the\nquality of crowdworkers' gold references. We conclude that the performance of\ntoday's LLMs can augment the CSS research pipeline in two ways: (1) serving as\nzero-shot data annotators on human annotation teams, and (2) bootstrapping\nchallenging creative generation tasks (e.g., explaining the underlying\nattributes of a text). In summary, LLMs are posed to meaningfully participate\nin social science analysis in partnership with humans.", + "authors": "Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, Diyi Yang", + "published": "2023-04-12", + "updated": "2024-02-26", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.LG" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2206.13757v1", + "title": "Flexible text generation for counterfactual fairness probing", + "abstract": "A common approach for testing fairness issues in text-based classifiers is\nthrough the use of counterfactuals: does the classifier output change if a\nsensitive attribute in the input is changed? Existing counterfactual generation\nmethods typically rely on wordlists or templates, producing simple\ncounterfactuals that don't take into account grammar, context, or subtle\nsensitive attribute references, and could miss issues that the wordlist\ncreators had not considered. In this paper, we introduce a task for generating\ncounterfactuals that overcomes these shortcomings, and demonstrate how large\nlanguage models (LLMs) can be leveraged to make progress on this task. We show\nthat this LLM-based method can produce complex counterfactuals that existing\nmethods cannot, comparing the performance of various counterfactual generation\nmethods on the Civil Comments dataset and showing their value in evaluating a\ntoxicity classifier.", + "authors": "Zee Fryer, Vera Axelrod, Ben Packer, Alex Beutel, Jilin Chen, Kellie Webster", + "published": "2022-06-28", + "updated": "2022-06-28", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.CY" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2402.07688v1", + "title": "CyberMetric: A Benchmark Dataset for Evaluating Large Language Models Knowledge in Cybersecurity", + "abstract": "Large Language Models (LLMs) excel across various domains, from computer\nvision to medical diagnostics. However, understanding the diverse landscape of\ncybersecurity, encompassing cryptography, reverse engineering, and managerial\nfacets like risk assessment, presents a challenge, even for human experts. In\nthis paper, we introduce CyberMetric, a benchmark dataset comprising 10,000\nquestions sourced from standards, certifications, research papers, books, and\nother publications in the cybersecurity domain. The questions are created\nthrough a collaborative process, i.e., merging expert knowledge with LLMs,\nincluding GPT-3.5 and Falcon-180B. Human experts spent over 200 hours verifying\ntheir accuracy and relevance. Beyond assessing LLMs' knowledge, the dataset's\nmain goal is to facilitate a fair comparison between humans and different LLMs\nin cybersecurity. To achieve this, we carefully selected 80 questions covering\na wide range of topics within cybersecurity and involved 30 participants of\ndiverse expertise levels, facilitating a comprehensive comparison between human\nand machine intelligence in this area. The findings revealed that LLMs\noutperformed humans in almost every aspect of cybersecurity.", + "authors": "Norbert Tihanyi, Mohamed Amine Ferrag, Ridhi Jain, Merouane Debbah", + "published": "2024-02-12", + "updated": "2024-02-12", + "primary_cat": "cs.AI", + "cats": [ + "cs.AI", + "cs.CR" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2404.07981v1", + "title": "Manipulating Large Language Models to Increase Product Visibility", + "abstract": "Large language models (LLMs) are increasingly being integrated into search\nengines to provide natural language responses tailored to user queries.\nCustomers and end-users are also becoming more dependent on these models for\nquick and easy purchase decisions. In this work, we investigate whether\nrecommendations from LLMs can be manipulated to enhance a product's visibility.\nWe demonstrate that adding a strategic text sequence (STS) -- a carefully\ncrafted message -- to a product's information page can significantly increase\nits likelihood of being listed as the LLM's top recommendation. To understand\nthe impact of STS, we use a catalog of fictitious coffee machines and analyze\nits effect on two target products: one that seldom appears in the LLM's\nrecommendations and another that usually ranks second. We observe that the\nstrategic text sequence significantly enhances the visibility of both products\nby increasing their chances of appearing as the top recommendation. This\nability to manipulate LLM-generated search responses provides vendors with a\nconsiderable competitive advantage and has the potential to disrupt fair market\ncompetition. Just as search engine optimization (SEO) revolutionized how\nwebpages are customized to rank higher in search engine results, influencing\nLLM recommendations could profoundly impact content optimization for AI-driven\nsearch services. Code for our experiments is available at\nhttps://github.com/aounon/llm-rank-optimizer.", + "authors": "Aounon Kumar, Himabindu Lakkaraju", + "published": "2024-04-11", + "updated": "2024-04-11", + "primary_cat": "cs.IR", + "cats": [ + "cs.IR", + "cs.AI", + "cs.CL" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2303.01248v3", + "title": "Can ChatGPT Assess Human Personalities? A General Evaluation Framework", + "abstract": "Large Language Models (LLMs) especially ChatGPT have produced impressive\nresults in various areas, but their potential human-like psychology is still\nlargely unexplored. Existing works study the virtual personalities of LLMs but\nrarely explore the possibility of analyzing human personalities via LLMs. This\npaper presents a generic evaluation framework for LLMs to assess human\npersonalities based on Myers Briggs Type Indicator (MBTI) tests. Specifically,\nwe first devise unbiased prompts by randomly permuting options in MBTI\nquestions and adopt the average testing result to encourage more impartial\nanswer generation. Then, we propose to replace the subject in question\nstatements to enable flexible queries and assessments on different subjects\nfrom LLMs. Finally, we re-formulate the question instructions in a manner of\ncorrectness evaluation to facilitate LLMs to generate clearer responses. The\nproposed framework enables LLMs to flexibly assess personalities of different\ngroups of people. We further propose three evaluation metrics to measure the\nconsistency, robustness, and fairness of assessment results from\nstate-of-the-art LLMs including ChatGPT and GPT-4. Our experiments reveal\nChatGPT's ability to assess human personalities, and the average results\ndemonstrate that it can achieve more consistent and fairer assessments in spite\nof lower robustness against prompt biases compared with InstructGPT.", + "authors": "Haocong Rao, Cyril Leung, Chunyan Miao", + "published": "2023-03-01", + "updated": "2023-10-13", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + }, + { + "url": "http://arxiv.org/abs/2305.11595v3", + "title": "Examining Inter-Consistency of Large Language Models Collaboration: An In-depth Analysis via Debate", + "abstract": "Large Language Models (LLMs) have shown impressive capabilities in various\napplications, but they still face various inconsistency issues. Existing works\nprimarily focus on the inconsistency issues within a single LLM, while we\ncomplementarily explore the inter-consistency among multiple LLMs for\ncollaboration. To examine whether LLMs can collaborate effectively to achieve a\nconsensus for a shared goal, we focus on commonsense reasoning, and introduce a\nformal debate framework (FORD) to conduct a three-stage debate among LLMs with\nreal-world scenarios alignment: fair debate, mismatched debate, and roundtable\ndebate. Through extensive experiments on various datasets, LLMs can effectively\ncollaborate to reach a consensus despite noticeable inter-inconsistencies, but\nimbalances in their abilities can lead to domination by superior LLMs.\nLeveraging a more advanced LLM like GPT-4 as an authoritative judge can boost\ncollaboration performance. Our work contributes to understanding the\ninter-consistency among LLMs and lays the foundation for developing future\ncollaboration methods. Codes and data are available at\nhttps://github.com/Waste-Wood/FORD", + "authors": "Kai Xiong, Xiao Ding, Yixin Cao, Ting Liu, Bing Qin", + "published": "2023-05-19", + "updated": "2023-10-18", + "primary_cat": "cs.CL", + "cats": [ + "cs.CL", + "cs.AI" + ], + "category": "LLM Fairness" + } +] \ No newline at end of file