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"gt": "Commonsense reasoning is the ability to make logi- cal deductions about concepts encountered in daily life, and is considered as a critical property of intel- ligent agents (Davis and Marcus, 2015). Concepts are mental representations of classes and are ex- pressed using words in a language (Liu et al., 2023). Given the inputs, the GCR task requires a model to generate a high quality sentence that is gram- matical and adheres to commonsense, evaluated by its similarity to a set of human-written reference sentences covering the same set of concepts (Lin et al., 2020). Often there exists multiple relationships between a given set of concepts, leading to alternative rea- soning paths that take diverse view points. For ex- ample, given the four concepts dog, frisbee, throw and catch, different sentences can be generated as Dog; Catch; Frisbee; Throw A dog leaps to catch a thrown frisbee. The dog catches the frisbee when the boy throws it. A man throws away his dog's favourite frisbee expecting him to catch it in the air. A\u00a0dog catches\u00a0a\u00a0frisbee thrown\u00a0to it. A dog catches a frisbee thrown by its owner. A dog jumps in the air to catch a frisbee thrown by its owner. Figure 1: An example of diverse generated sentences sets in CommonGen (Lin et al., 2020) dataset. The gen- eration shown at the bottom (in green ) are considered by human annotators to be more diverse than those at the top (in red ). shown in Figure 1. Although all sentences shown in Figure 1 are grammatical, the bottom set ex- presses diverse view points (e.g. from the dog\u2019s as well as the man\u2019s) compared to the set at the top. Apart from the generation quality, diversity is also an important factor in text generation because the low-diversity texts tend to be dull, repetitive or biased towards a particular view point (Tevet and Berant, 2021). Diversity is an important considera- tion in many Natural Language Generation (NLG) applications, such as story generation (Li et al., 2018), paraphrase generation (Gupta et al., 2018), and GCR (Yu et al., 2022; Liu et al., 2023). In GCR tasks, the input text often provides insuffi- cient information to support diverse reasoning and generate multiple plausible outputs. Therefore, the diversity present in GCR task enables the explo- ration of different perspectives or all possible out- comes for a real-world situation. Existing methods promote diversity through special decoding strate- gies, such as nucleus sampling (Holtzman et al., 2019), or encoding interventions such as random noise injection (Gupta et al., 2018) or Mixture of Experts (MoE) approaches (Shen et al., 2019). We propose In-Context Diversification (ICD), a computationally-efficient and accurate method to improve the diversity in GCR, where the sentences are generated from a pre-trained LLM, and strikes arXiv:2404.16807v1 [cs.CL] 25 Apr 2024 a fine-balance between the output diversity and quality. ICD uses an ICL approach to increase the diversity of the sentences generated by an LLM, while maintaining the quality of the generation. ICD is a two-step process where it first lets an LLM to freely generate high-quality sentences that are grammatical, commonsense bearing and cover all the given input concepts. Next, ICD uses a user- specified diversity metric to evaluate the diversity of the generated sentences. If the diversity is low, ICD provides feedback to the LLM, instructing it to generate more diverse sentences considering the already generated sentences. Given that ICD is using LLMs to generate di- verse sentences via ICL and without updating the parameters of the LLMs, an interesting and open question is whether an LLM can accurately judge the diversity of a given set of sentences, covering a common set of concepts. To answer this ques- tion, we conduct an experiment where we instruct GPT3.5-turbo to judge the diversity of the set of input sentences according to a five-scale grading system, and convert the predicted grades into bi- nary judgements (i.e. diverse vs. non-diverse). We compare the LLM-assigned grades against those by a group of human annotators, and find a moderate- level (Cohen\u2019s Kappa of 0.409) agreement between human vs. LLM judgements, demonstrating that LLMs can indeed be instructed to obtain diversity judgements for GCR tasks. We evaluate ICD on three GCR tasks/datasets: CommonGen (Lin et al., 2020), ComVE (Wang et al., 2020), and DimonGen (Liu et al., 2023). We find that our proposed ICD balances diversity and quality appropriately, improving their harmonic mean by at least 6% over that of a default base- line. Moreover, the sentences generated by ICD can be used as training data to improve diversity in a Seq2Seq model (Sutskever et al., 2014; Lewis et al., 2020), producing results that are comparable to the models that are trained on knowledge graphs or human-written text corpora (Liu et al., 2021; Fan et al., 2020; Li et al., 2021).", |
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"main_content": "Diverse Text Generation. A variety of methods have been proposed to enhance the diversity of NLG. Sampling-based decoding is an effective method to increase the generation diversity. Holtzman et al. (2019) proposed nucleus sampling to generate diverse content at the generation stage. Truncated sampling (Fan et al., 2018) prunes and then samples the tokens based on the probability distribution. Furthermore, Shen et al. (2019) proposed an MoE approach to diversify translation outputs. Moreover, incorporating external corpora in the MoE further promotes diversity, such as by using a knowledge graph (Yu et al., 2022; Hwang et al., 2023) or by a collection of retrieved sentences (Liu et al., 2023). Although LLMs have reported superior performance in numerous Natural Language Processing (NLP) tasks (Touvron et al., 2023; OpenAI, 2023b,a), to the best of our knowledge, diversifying their generations in commonsense reasoning with ICL has not been explored in prior work on GCR. In-Context Learning. Recent studies demonstrate that LLMs can exhibit robust few-shot performance on a variety of downstream tasks through ICL (Brown et al., 2020). ICL is a technique for instructing an LLM using one or more examples for a particular text generation task. The generated text is conditioned on both the input as well as the instruction prompt. Wang et al. (2023) show that in ICL, label words in the demonstration examples function as anchors, which aggregate semantic information to their word representations in the shallow (closer to the input) layers, while providing that information to the final predictions performed by the deeper (closer to the output) layers. In contrast to fine-tuning-based methods, ICL is computationally lightweight because it does not update the parameters of the LLM. Therefore, ICL is an attractive method when integrating task-specific knowledge to an LLM by simply changing the prompt and the few-shot examples (Dong et al., 2022). 3 In-context Diversification We consider the problem of generating a set of diverse sentences that express commonsense reasoning, either by covering a set of given concepts (in CommonGen and DimonGen) or by providing an explanation for a given counterfactual statement (in ComVE). Formally, given a sequence (a set of concepts or a statement) X = {x1, . . . , xm}, the goal of GCR is to generate a set of grammatically correct and commonsense bearing sentences Y = {y1, . . . , yn}, where yi is the i-th output generated by the model with probability p(yi|X). Moreover, we require that the generated sentences {y1, . . . , yn} to be lexically as well as semantically diverse. Default Examples: Given several key words: [SRC], generate one coherent sentences using background commonsense knowledge: [TGT] Test instruction: Given several key words: [INPUT], generate one coherent sentence using background commonsense knowledge: [OUTPUT] Diversified Examples: Given several key words: [SRC], generate one coherent sentence using background commonsense knowledge: [TGT] Test instruction: Step1: Given several key words: [INPUT], generate [N] different and coherent sentences using background commonsense knowledge: [PRV] (If the diversity of [PRV] is low) Step2: You have generated the following sentences: [PRV], try to provide other reasonable sentences: [OUTPUT] (a) (b) Figure 2: An example of default and diversified prompts is shown for an instance selected from the CommonGen dataset. Here, the default prompt shown in Figure 2a is taken from Li et al. (2023). Few-shot examples are included in each prompt where [SRC] denotes the set of input concepts and [TGT] the corresponding sentences in CommonGen. For a given set of [INPUT] concepts, the LLM is then required to generate sentences at the slot [OUTPUT]. As shown in Figure 2b, ICD uses the diversified prompt, which operates in two steps. Step 1 generates a set of [N] sentences, [PRV]. We check for the diversity among the sentences in [PRV], and if it is low, we use the prompt in Step 2 to generate the final set of sentences. 3.1 Sentence Generation To explain our proposed ICD, let us consider GCR on CommonGen, where we must generate a set of sentences Y, such that each sentence contains all of the input concepts X as shown in Figure 2a. Given an LLM, we can design a prompt that contains a task-specific instruction and one or more examples containing the input concepts (denoted by [SRC] in Figure 2) and the corresponding human-written sentences containing all given input concepts (denoted by [TGT]) to instruct the LLM to generate output sentences Y (denoted by [OUTPUT]) for a given set of input concepts X (denoted by [INPUT]). We refer to a prompt of this nature as a default prompt, and the corresponding set of generated sentences by Sdef. Note that the default prompt does not necessarily guarantee that the generated set of sentences will be diverse and an LLM could return sentences that are highly similar to each other. To address this issue, we propose a diversified prompt as shown in Figure 2b. Specifically, the diversified prompt operates in two steps. In Step 1, we require that the LLM generate N sentences that are different, in addition to being coherent and commonsense bearing. Next, we use a suitable diversity metric to evaluate the level of diversity among the generated set of sentences. If the diversity of the generated senAlgorithm 1 In-Context Diversification (ICD) Input: Generated sets of sentences Sdef and Sdiv, respectively from default and diversified prompts, the number of desired output sentences N, and a diversity metric f. Output: Output set of sentences S\u2217 S\u2217\u2190\u2205 \u03b1 \u21900 for S \u2208(Sdef \u222aSdiv) do if (|S| == N) \u2227(f(S) \u2265\u03b1) then \u03b1 \u2190f(S) S\u2217\u2190S end if end for return S\u2217 tences is low, in Step 2, we show those sentences to the LLM and instruct it to generate sentences that are different to those. As the criteria for triggering Step 2, we check whether the exact same sentence has been generated multiple times by the LLM during Step 1. The final set of generated sentences is denoted by Sdiv. 3.2 Diversity-based Sampling Because of the limited availability of humanwritten reference sentences for evaluating GCR models, there exists a trade-off between quality vs. diversity when generating sentences for GCR tasks.1 Simply maximising for diversity often leads to generations that do not cover the input concepts in a natural way. For example, a randomly selected set of sentences would be highly diverse, yet unlikely to capture the input concept sets. On the other hand, if we force an LLM to generate sentences that contain all of the input concepts, it might find difficult to generate semantically diverse sentences and resort to trivial lexical or syntactic diversity tricks such as morphological inflections or word-order permutations. To address this issue, we propose a diversitybased sampling method shown in Algorithm 1. Consider that the default prompt provides a set Sdef of sentences that have not been optimised for diversity (likely to have a higher quality), while on the other hand the diversified prompt provides a set Sdiv of sentences that are further refined for diversity (likely to have a higher diversity). We wish to find a set of sentences that simultaneously satisfies the following criteria: (a) must contain exactly N sentences, as specified by the user, and (b) must have a high diversity score, measured using a user-specified diversity metric f(\u2208R\u22650). We formalise this as a subset search problem, where 1This trade-off is further empirically verified in \u00a7 5.1. we compute the union Sdef \u222aSdiv and search for the subset S\u2217that jointly satisfies those criteria following the procedure detailed in Algorithm 1. Although the total number of subsets of size N is \u0000|Sdef\u222aSdiv| N \u0001 , it is sufficiently small for the values of N in our GCR tasks, which makes this subset search fast in practice. 4 Experimental Settings 4.1 Tasks and Datasets We evaluate ICD on three GCR tasks as follows. Constrained Commonsense Reasoning: In CommonGen (Lin et al., 2020) benchmark, a model is required to generate a sentence covering a given set of concepts such that background commonsense knowledge associated with the input concepts is reflected. This dataset contains 35K distinct concept sets (train = 32651, dev = 993, and test = 1497) with corresponding human written sentences (train = 67389, dev = 4018, and test = 6042). Each instance contains on average 3-5 input concepts. Commonsense Explanation Reasoning: ComVE (Wang et al., 2020) is part of the SemEval 2020 commonsense validation task, where for a given counterfactual statement, a model is required to generate an explanation providing a reason describing why the statement is nonsensical. This dataset contains 10K (train = 8532, dev = 476, and test = 992) examples, where each example contains three reference outputs. Diversified GCR: DimonGen (Liu et al., 2023) involves generating diverse sentences that describe the relationships between two given concepts. It is a challenging task because it requires generating reasonable scenarios for a given pair of concepts without any context. This dataset contains 17109 instances (train = 15263, dev = 665, test = 1181), where each instance has 3-5 references. 4.2 Evaluation Metrics We measure both the quality and diversity of the sentences generated by models using the metrics described next. 4.2.1 Quality Metrics We compare a generated sentence by a model against a set of human-written references to evaluate the quality of the generation using several metrics: BLEU (Papineni et al., 2002) measures n-gram precision against human reference texts, SPICE (Anderson et al., 2016) measures the semantic propositional overlap between two sentences, and BERTScore (Zhang et al., 2020) uses contextualised word embeddings to measure the semantic similarity between tokens in two sentences. In alignment with prior works (Yu et al., 2022; Liu et al., 2023; Hwang et al., 2023), when multiple candidate sentences are generated for a test case, we select the highest-scoring candidate for evaluating quality. 4.2.2 Diversity Metrics Pairwise Diversity: We use self-BLEU (Zhu et al., 2018) to measure n-gram overlap among sentences within each generated set. The metric computes the average sentence-level similarity between all pairwise combinations of the generations in the generation set. Note that unlike BLEU, self-BLEU does not require human generated references for measuring diversity. We use self-BLEU3/4 (corresponding to n = 3 and 4) in our experiment. Lower self-BLEU scores indicate higher lexical diversity. Corpus Diversity: To measure the variety within our generated text corpus, we employ Distinctk (Li et al., 2016), which calculates the ratio of unique k-grams to the total number of k-grams. This metric is particularly useful for adjusting the bias of LLMs toward generating longer sequences, ensuring that diversity is not artificially inflated by the sentence length. Additionally, we use Entropyk to evaluate the distributional uniformity of kgram occurrences, considering word frequencies for a more nuanced view of diversity. Higher Distinct-k and Entropy-k scores indicate higher diversity. Semantic Diversity: All previously described diversity metrics are limited to evaluating lexical diversity. To measure diversity at a semantic level, we propose self-cosSim, which is the average pairwise cosine similarity between generated sentences, computed using sentence embeddings obtained from SimCSE (Gao et al., 2021). Likewise, we define the self-BERTScore as a diversity metric that averages the BERTScores for all generated sentence pairs. Lower self-cosSim and self-BERTScore values indicate higher semantic diversity. 4.2.3 Combined Metrics We would prefer GCR models that have both high quality and high diversity. To incoporate both aspects into a single metric, we compute the Harmonic Mean between (a) the self-BLEU-4 as the diversity metric, and (b) BERTScore as the quality metric. As discussed in \u00a7 3.2, there exists a tradeoff between quality and diversity in GCR. Therefore, the harmonic mean is suitable when averaging quality and diversity scores.2 Alihosseini et al. (2019) proposed Fr\u00b4 echet BERT Distance (FBD) as a joint metric for simultaneously measuring both the quality and diversity of NLG. FBD is inspired by the Fr\u00b4 echet Inception Distance (FID), proposed by Heusel et al. (2017), for measuring the quality of image generation. Specifically, FBD computes the pooler output3 of a sentence as its embedding (Devlin et al., 2019) and represents a set of sentences using the mean vector and the covariance matrix computed from their sentence embeddings. Next, Wasserstein-2 distance is computed between the set of reference sentences and the set of generated sentences, which captures both the distance between the means as well as variance in the distributions. Lower FBD scores indicate high combined performance. 4.3 Implementation Details We use GPT3.5-turbo and Vicuna-13b-v1.54 as LLMs with temperature set to 1.0 in our experiments. By using two LLMs with significantly differing number of parameters and by including, Vicuna, an open source LLM, we plan to improve the reliability and reproducibility of our results. Max response length is set to 25 tokens. The inference times for CommonGen, ComVE and DimonGen datasets are respectively 5-6, 2-3 and 1-2 hours. The cost of running ICD with GPT3.5-turbo are ca. $6, $4 and $4 respectively for CommonGen, ComVE and DimonGen datasets. On the other hand, the costs of fine-tuning on GPT3.5-turbo are much higher at $58.8 for CommonGen, $24.7 for ComVE and $32.0 for DimonGen. Moreover, fine-tuning with LoRA (Hu et al., 2022) with rank of 8 and alpha of 16 on Vicuna takes ca. 34 hours. We use BART-large5 for MoE-based models. We use the GPT3.5-turbo to generate sentences for the CommonGen train/dev/test sets using the de2We use self-BLEU-4 for diversity and BERTScore for quality in Harmonic Mean due to their reliability shown in preliminary evaluations. Other metric pairs are in Appendix D. 3The last layer\u2019s hidden-state of the first token of the sequence is further processed by a Linear layer and a Tanh activation function. 4https://huggingface.co/lmsys/vicuna-13b-v1.5 5https://huggingface.co/facebook/bart-large fault, diversified and for ICD. For model training, we use the Adam optimiser (Kingma and Ba, 2015) with a batch size of 64, a learning rate of 3e-5 and a beam size of 5. All of the MoE-based models are trained for 20 epochs and required to generate k = 3 sentences. All experiments, except with GPT3.5-turbo, are conducted on a single RTX A6000 GPU. 5 Results and Discussion 5.1 Commonsense Generation We compare the commonsense generations made by ICD against those using the default and diversified prompts. For this purpose, we use GPT3.5-turbo as the LLM and use the same 10 few-shot examples in all prompts for ICL. Further templates of the default and diversified prompts used for each task are given in Appendix E. To assess the impact of ICL, we compare against finetune method, wherein GPT3.5-turbo is fine-tuned on the entire training set in each dataset. Specifically, we use multiple human-written sentences, available in the training data for the three datasets to separately fine-tune the models for each task. It is noteworthy that the fine-tune method uses a substantially larger dataset for training (e.g., 67,389 sentences from CommonGen) compared to the 10 examples used by the ICL-based approaches. We use self-BLEU-3 as the diversity metric f in Algorithm 1 for ICD in this evaluation. The outcomes, presented in Table 1, highlight the diversity and quality metrics of these methods across the CommonGen, ConVE, and DimonGen datasets. Additionally, a human baseline is introduced to evaluate the diversity of sentences written by humans, where we pair-wise compare the human-written sentences for each input in the instances in the benchmark datasets using diversity metrics. Note that however, the human baseline must not be considered as an upper-bound for diversity because there are only a smaller number of human-written sentences per instance in the benchmark datasets. From Table 1, we see that fine-tune generates sentences that have high semantic and corpus diversity, and outperforms the human baseline. However, recall that fine-tune requires a much larger training set and is computationally costly compared to all ICL-based methods. Moreover, we see that ICD can strike a good balance between quality and diversity in the sentences generated. Among the ICL-based methods, ICD achieves the best diSemantic Diversity \u21d3 Corpus Diversity \u21d1 Pairwise Diversity \u21d3 Quality \u21d1 Combined self-cosSim self-BERTScore Entropy-4 Distinct-4 self-BLEU-3 self-BLEU-4 BLEU-3 BLEU-4 SPICE BERTScore Harmonic \u21d1 FBD \u21d3 CommonGen Human 67.3 60.6 10.9 91.0 25.4 17.6 Fine-tune 64.7 55.9 11.4 91.1 26.9 17.9 41.2 32.1 30.3 64.2 72.1 51.9 default 93.3 88.7 10.2 53.7 77.2 72.4 50.8 40.9 30.1 70.4 39.6 60.2 diversified 85.2 69.8 11.0 83.7 44.4 34.9 44.3 34.6 28.5 65.0 65.4 53.9 ICD 83.5 66.2 11.0 88.5 31.0 21.0 47.4 37.7 29.1 67.4 72.7 51.8 ComVE Human 62.7 47.0 9.6 96.1 12.4 8.1 Fine-tune 59.8 42.6 9.8 95.2 13.4 10.3 27.4 19.4 33.1 53.7 67.2 47.6 default 83.9 73.5 9.6 74.3 50.8 45.2 27.5 19.7 36.2 55.1 54.9 50.9 diversified 76.1 56.5 9.7 88.0 23.2 16.5 30.5 21.8 35.8 56.5 67.4 47.9 ICD 72.5 51.1 9.8 90.1 13.7 8.7 29.0 20.8 36.1 55.5 69.0 48.7 DimonGen Human 56.8 47.0 10.1 85.6 14.7 8.7 Fine-tune 43.4 33 10.4 98.7 6.8 3.4 17.7 10.7 15.5 42 58.5 51.6 default 75.7 71.3 10 83.2 43.4 37.3 15.9 9.5 16.4 44.5 52.1 68.2 diversified 57.1 46.9 10.5 95.9 11.2 6.5 11.4 6.4 15.2 39.9 55.9 69.0 ICD 56.7 45.7 10.4 96.3 6.5 3.5 13.2 7.6 15.4 41.7 58.2 68.0 Table 1: Diversity and quality scores on CommonGen, ComVE and DimonGen with GPT3.5-turbo LLM. Best results on each task for each metric are shown in italics, while the best performing ICL results are shown in bold. versity scores on all diversity metrics in all three datasets. It also exhibits higher diversity compared against the human-written references. Moreover, ICD outperforms default and diversified according to the Combined metrics. ICD also achieves a Harmonic Mean comparable to that of the fine-tune baseline. Although default reports the best quality scores, it has low diversity, and is consistently outperformed by diversified and ICD on diversity metrics. On the other hand, diversified generally scores lower on the quality metrics. Compared to default and diversified, ICD enhances generation diversity while maintaining a satisfactory level of quality. ICD is also more stable to the sampling method such as temperature than fine-tune, as shown in Appendix B. Note that fine-tune is not an ICL setting (the focus of this paper) and is included only as a baseline to demonstrate the level of performance that can be achieved by finetuning on a much larger dataset. Despite this, ICD outperforms fine-tune on the Pairwise Diversity in all three datasets, and Combined metrics in the CommonGen dataset. As an open source alternative LLM to GPT3.5-turbo, we repeat this evaluation with Vicuna-13b (Zheng et al., 2023) in Table 2. The same 10 few-shot examples as used with GPT3.5-turbo are used in this experiment for the ICL-based methods. Full table on three datasets are shown in Appendix C. Table 2 reconfirms ICD\u2019s ability to balance both quality and diversity according to the Combined metrics (i.e. Harmonic Mean and FBD) on this dataset. Interestingly, we see that Method SCS \u21d3 SBS \u21d3 E-4\u21d1 D-4\u21d1 SB-3\u21d3 BLEU-3\u21d1 SPICE\u21d1 HM \u21d1 FBD \u21d3 Fine-tune 59.6 49.9 11.4 93.3 22.8 35.8 27.6 69.9 52.4 Default 82.2 73.8 10.9 74.9 52.9 44.6 29.1 60.2 56.2 Diversified 59.1 53.3 11.3 91.3 23.6 32.6 24.3 68.6 53.2 ICD 59.3 49.8 11.3 93.7 11.3 34.2 25.5 73.4 51.0 Table 2: GCR on CommonGen using Vicuna-13b. ICD uses self-BLEU-3. Here, SCS: self-CosSim, SBS: selfBERTScore, E-4: Entropy-4, D-4: Distinct-4, SB-3: self-BLEU3, HM: Harmonic Mean. Best results for each metric are shown in italics, while the best performing ICL results are shown in bold. Method SCS \u21d3 SBS \u21d3 E-4\u21d1 D-4\u21d1 SB-3\u21d3 BLEU-3\u21d1 SPICE\u21d1 HM \u21d1 FBD \u21d3 self-BLEU-3 83.5 66.2 11.0 88.5 31.0 47.4 29.1 72.7 51.8 self-CosSim 81.0 70.1 10.9 82.5 44.5 47.6 29.3 65.7 51.8 self-BERTScore 83.1 62.8 11.0 87.0 36.3 46.5 28.9 69.6 51.8 Table 3: Comparing the effect of using different diversity metrics, f, in Algorithm 1 for ICD. We use GPT3.5-turbo as the LLM and the best results on CommonGen dataset are in bold. Here, SCS: self-CosSim, SBS: self-BERTScore, E-4: Entropy-4, D-4: Distinct-4, SB-3: self-BLEU3, HM: Harmonic Mean. methods that use Vicuna-13b to be more diverse compared to those that use GPT3.5-turbo, while the latter showing better generation quality. In Table 3, we use different diversity metrics as f in Algorithm 1 to study the effect on text generation of ICD. We see that self-BLUE-3 and self-CosSim perform similarly across the quality metrics. SelfBERTScore shows a slightly lower quality (BLEU3 and SPICE), which indicates some level of overfitting to the diversity metric being used. According to the combined metrics, any of those diversity metrics can be used with ICD to obtain comparable performance. Semantic Diversity \u21d3 Corpus Diversity \u21d1 Pairwise Diversity \u21d3 Quality \u21d1 Combined self-cosSim self-BERTScore Entropy-4 Distinct-4 self-BLEU-3 self-BLEU-4 BLEU-3 BLEU-4 SPICE BERTScore Harmonic Mean \u21d1 FBD \u21d3 KG-BART 42.1 30.9 32.7 EKI-BART 46.0 36.1 33.4 KFCNet-w/o FC 50.2 42.0 35.9 KFCNet 57.3 51.5 39.1 MoE 89.3 81.9 9.7 61.6 63.1 56.6 49.0 38.5 33.5 70.6 53.8 61.7 MoKGE 88.7 80.6 9.9 65.2 60.4 53.6 48.8 38.4 33.1 70.3 55.9 60.8 default+MoE 90.8 84.2 9.7 61.2 65.6 58.8 51.8 41.3 34.7 73.1 52.7 61.9 diversified+MoE 85.3 79.9 9.8 63.2 58.3 52.6 51.4 41.4 34.6 71.6 57.0 54.5 ICD+MoE 90.4 82.3 9.8 64.9 58.4 50.5 53.2 43.1 35.4 73.8 59.3 62.5 Table 4: Downstream evaluation of the LLM-generated sentences. Top block methods use human-generated resources for training, while the ones in the bottom block are trained on LLM-generated sentences. MoE approaches are shown in the middle block and bottom block. BART-large is used as the generator for MoE-based methods. Best results for each metric are shown in bold, while the best performing MoE for quality is shown in underline. Figure 3: Human vs. GPT3.5 diversity ratings for randomly sampled sets of sentences generated by ICD. Cohen\u2019s \u03ba = 0.409 indicates a moderate agreement. 5.2 Downstream Evaluation The experiments presented in \u00a7 5.1 show the ability of our proposed ICD to generate diverse and commonsense bearing sentences. Therefore, an important question with practical implications is whether we can use the sentences generated by ICD as additional training data to improve both diversity and quality of previously proposed models on the GCR task, which could be seen as a downstream (extrinsic) evaluation. For this purpose we select the MoE (Shen et al., 2019), which diversifies the generation by selecting outputs from a mixture of experts. Each expert is assigned a randomly generated sequence of tokens, which is used as a prefix for all inputs sent to that expert. For each input, an expert is selected according to the value of a latent variable, which is trained using the hard-EM algorithm. We follow Liu et al. (2023) and train three experts that retrieve sentences from the collection of sentences generated by ICD for concept sets in the CommonGen train split (210846 sentences in total). We use BART-large (Lewis et al., 2020) as the base model, which has shown to produce high quality commonsense generations (Zhang et al., 2023) as the generator for all experts (see Appendix A for further details). We denote this method by ICD+MoE. As baselines for comparisons, we repeat the above process using the sentences generated by default and diversified, which we denote respectively as default+MoE and diversified+MoE in Table 4. Moreover, we compare the performance against two previously proposed MoE models: MoE (Shen et al., 2019) and MoKGE (Yu et al., 2022). MoE relies solely on the base model, whereas MoKGE requires each expert to use different sets of concepts from the ConceptNet (Speer et al., 2017) knowledge graph (KG). Because Yu et al. (2022) do not evaluate their MoKGE method on CommonGen, we ran their original implementation6 on CommonGen and report results in Table 4. All previously proposed GCR methods are exclusively trained using human-created data (e.g. sentences written by human and/or manually compiled KGs such as ConceptNet), whereas the methods described thus far in this section are trained on sentences generated by an LLM (GPT3.5-turbo). Therefore, to evaluate the feasibility of using LLMgenerated sentences for training GCR models, we include the following previously proposed GCR models that are trained using a combination of corpora and KGs: KG-BART (Liu et al., 2021),EKIBART (Fan et al., 2020) and KFCNet (Li et al., 2021). For KFCNet, we present its two results \u2013 KFCNet w/o FC, which relies only on sentences including the input concepts, without further processing, and KFCNet, which additionally ranks candidates and adds contrastive modules for the encoder and the decoder (Li et al., 2021). However, note that those methods do not consider diversifica6https://github.com/DM2-ND/MoKGE Human: \u2022 The group will use the tool to make a piece of art out of metal. \u2022 I use a tool to cut a piece of metal out of the car. \u2022 The man used a piece of metal and the tools. Default: \u2022 A piece of metal is being used as a tool. \u2022 A piece of metal was used as a tool in the construction project. \u2022 A metal tool is being used to shape a piece of metal. ICD: \u2022 The piece of metal is essential for any handyman's toolkit. \u2022 The metal tool is a useful piece for working with metal. \u2022 With the right tools, any piece of metal can be transformed into something useful. CommonGen: Input: (piece, use, tool, metal) Human: \u2022 No one can digest electronic goods. \u2022 Electronic products must not be eaten. \u2022 You would die if you ate electronics. Default: \u2022 Electronic goods are not edible and are not meant for consumption. \u2022 Electronic goods are not edible and cannot be consumed as food. \u2022 Electronic goods are not edible and are meant for functional use rather than consumption. ICD: \u2022 Eating electronic goods can damage the digestive system and cause serious health issues. \u2022 It is not healthy or safe to eat electronic goods as they are made up of toxic materials. \u2022 Electronic goods are not edible and cannot be consumed as food. ComVE: Input: My friend like to eat electronic goods. Figure 4: Sentences generated by default prompt and ICD against those by humans on CommonGen and ComVE test instances. ICD generates more diverse and high quality sentences than default. tion, and do not report performance using diversity metrics. Therefore, we report only their published results for generation quality in Table 4. From Table 4 we see that diversified+MoE always outperforms the original MoE in all diversity metrics, which shows that sentences generated from LLMs can be used to diversify MoE-based GCR. ICD+MoE closely matches the performance of diversified+MoE on diversity metrics, while consistently outperforming both diversified+MoE and default+MoE on quality metrics. In particular, the quality metrics reported by ICD+MoE (underlined in Table 4) are competitive against those obtained by the models that are trained on human-compiled resources (in the top block), except against KFCNet. This finding hints at potential improvement gains for GCR by using hybrid training resources that combine both human-compiled and LLM-generated data, which we highlight as an interesting future research direction. 5.3 Diversity-Awareness of LLMs Given that we use LLMs to produce diverse generations via ICL, it remains an open question whether an LLM would agree with humans on the diversity of a given set of sentences. To answer this question, we use randomly selected 210 sentences (35 sets, each containing 6 sentences) generated by ICD (using self-BLEU-3 as the diversity metric) for the input concept sets in the CommonGen dataset. We instruct GPT3.5-turbo to rate the diversity of a given set of sentences according to five diversity ratings 1-5 with 1 being highly similar, while 5 being highly diverse.7 We provide the same instruction as the annotation guidelines for eight 7Detailed prompt templates are shown in Appendix E. human-annotators, who are graduate students in NLP. To reduce the subjective variability in human judgements, we average and then normalise the ratings following the Likert scale. In Figure 3, we plot the GPT-assigned ratings against those by humans. We further split the ratings into high vs. low diversity ratings depending on whether the rating is greater or lesser than 3. The majority of the data points are distributed along the diagonal quadrants and a Cohen\u2019s Kappa of 0.409 indicating a moderate level of agreement between GPT and human ratings for diversity. The generated sentences using the default prompt, ICD and the human references in CommonGen and ComVE datasets for a single test instance are shown in Figure 4. From Figure 4 we see that the sentences generated using the default prompt often results in significant token overlap, thereby lowering the diversity. On the other hand, ICD generates both lexically and semantically diverse sentences, covering the diverse viewpoints in the human references. 6 Conclusion We proposed, ICD, an ICL-based method for achieving the optimal balance between diversity and quality in text generation via LLMs. Our experiments, conducted on three GCR tasks, demonstrate that ICD significantly improves the diversity without substantially compromising the quality. Furthermore, we found that by training on the sentences generated by ICD, we can improve diversity in previously proposed GCR methods. 7 Limitations This study primarily focuses on the generation of English sentences using pre-trained LLMs, a limitation shaped by the datasets we employed. Specifically, we used the ComVE (Wang et al., 2020), CommonGen (Lin et al., 2020) and DimonGen (Liu et al., 2023) datasets, which are well-regarded for evaluating diversified commonsense reasoning in English. Therefore, our evaluation of the generation quality was limited to English, which is a morphologically limited language. Future research could expand this scope to include multilingual pretrained models, thereby encompassing a broader linguistic spectrum. Our approach is primarily geared towards optimizing the trade-off between diversity and quality in text generation. Consequently, we maintained consistent default instructions across all experiments, adopting the standard commonsense generation prompts used in Li et al. (2023) as our default instructions. We conducted our experiments using both a closed model (i.e. GPT3.5-turbo) as well as an open-source one (i.e. Vicuna-13b-v1.5) to promote the reproducibility of our results, which are reported using multiple public available benchmarks. However, there exist many other LLMs with varying numbers of parameters and trained on different corpora. Therefore, we consider it is important to evaluate our proposed method on a broad range of LLMs to verify the generalisability of our proposed method. However, conducting such a broad analysis can be computationally costly and expensive. For example, although GPT-4 is known to have superior text generation capabilities, it incurs substantially greater costs (being 30 times more expensive than GPT3.5-turbo at the current pricing). Nevertheless, ICD is adaptable and could be extended to other LLMs. 8 Ethical Considerations In this work, we did not create or release any manually annotated data. Our work is based on the publicly available datasets, CommonGen, ComVE, and DimonGen. To the best of our knowledge, no ethical issues have been reported for those datasets. Therefore, we do not foresee any data-related ethical issues arising from our work. However, LLMs are known to generate responses that may reflect societal biases and potentially harmful content. We have not verified whether the GPT3.5-turbo and Vicuna-13b LLMs that we use in our experiments have similar problems. Therefore, it is important to test on existing benchmarks for social biases and harmful generations before the proposed method is deployed to diversify existing GCR methods used by human users. To elicit human judgements of diversity for the sentences generated by ICD, we use annotators who are familiar with working with LLMs. It is possible that their subjective (and possibly biased) viewpoints might have influenced the ratings provided. Therefore, it will be important to conduct the evaluation involving a group of annotators with different backgrounds to validate the findings reported in this analysis." |
