Accelerating Scientific Literature Reviews with NVIDIA NIM Microservices for LLMs
A well-crafted systematic review is often the initial step for researchers exploring a scientific field. For scientists new to this field, it provides a structured overview of the domain. For experts, it refines their understanding and sparks new ideas. In 2024 alone, 218,650 review articles were indexed in the
Web of Science
database, highlighting the importance of these resources in research.
Completing a systematic review significantly enhances a researcher’s knowledge base and their academic impact. However, traditional review writing requires collecting, reading, and summarizing large volumes of academic articles on a specific topic. Due to the time-consuming nature of this manual exercise, the scope of processed literature is often confined to dozens or a few hundred articles. Interdisciplinary content—frequently outside the researcher’s area of expertise—adds another layer of complexity.
These challenges make it increasingly difficult to create comprehensive, reliable, and impactful systematic reviews.
The advent of large language models (LLMs) offers a groundbreaking solution, enabling the rapid extraction and synthesis of information from extensive literature. Participating in the
Generative AI Codefest Australia
provided a unique opportunity to explore this idea with support from NVIDIA AI experts to leverage
NVIDIA NIM microservices
for accelerating literature reviews. This enabled the rapid testing and fine-tuning of several state-of-the-art LLMs for our literature analysis process.
Testing the potential of LLMs for processing papers
As a research group specializing in physiological ecology within the
ARC Special Research Initiative Securing Antarctica’s Environmental Future (SAEF)
, we embarked on writing a review of the literature on the global responses of non-vascular plants, such as moss or lichen, to wind.
However, we quickly faced a challenge: many relevant articles on wind-plant interactions failed to explicitly mention these key words in their titles or abstracts, which are typically used as primary filters during literature screening. A comprehensive analysis of the topic required manually reading the full text of each article—a highly time-consuming process.
We decided to explore the potential of using LLMs to extract content specifically related to wind-plant interactions from the articles. To achieve this, we implemented a simple Q&A application based on the
LlaMa 3.1 8B Instruct NIM microservice
(Figure 1). This enabled us to get an initial prototype quickly.
This first prototype, processing the papers sequentially, was extremely useful to craft and optimize the prompts to extract key information from each article.
Figure 1. Initial simple paper processing pipeline using an LLM NIM microservice at its core
To validate the accuracy of extracted information, we initially manually validated the results. When no significant errors were found in the test dataset, we identified opportunities to further enhance the efficiency of key information extraction using LLMs (Figure 2). These include converting the papers from a PDF format to structured JSON; extracting images, tables, and charts; and using parallel processing to speed up the processing of papers.
Figure 2. Enhanced paper processing pipeline incorporating NV-ingest and parallel processing of papers
Enhancing the performance of LLMs for more efficient information extraction
By using NVIDIA NIM microservices for LLMs and
nv-ingest
, we deployed LLMs and a data ingestion pipeline in our local environment with eight NVIDIA A100 80-GB GPUs. We also fine-tuned the models using low-rank adaptation (LoRA) to improve the accuracy of information extraction from the papers.
We compiled a dataset of over 2K scientific articles related to the targeted research domain, sourced from the
Web of Science
and
Scopus
databases. Over a week during Generative AI Codefest, we focused on experimenting with various strategies to optimize the efficiency and accuracy of key information extraction from these articles.
Best-performing model
To determine the best-performing model, we tested a range of instruction-based and general-purpose LLMs from the NVIDIA API Catalog on a set of randomly selected articles. Each model was assessed for its accuracy and comprehensiveness in information extraction.
Ultimately, we determined that Llama-3.1-8B-instruct was the most suitable for our needs.
Processing speed
We developed a Q&A module using streamlit to answer user-defined research-specific questions.
To further improve processing speed, we implemented parallel processing of the prompts sent to the LLM engine and used KV-caching, which significantly accelerated the computation time by a factor of 6x when using 16 threads.
Extraction content types
We used nv-ingest to extract content from the original PDFs, including text, figures, tables, and charts, into structured JSON files. This enabled information extraction beyond text content, enabling a more comprehensive context for answering the questions.
Using JSON files instead of the original PDF files during inference also had a significant effect on lowering the processing time, by an additional factor of 4.25x.
Results
Thanks to these improvements, we significantly reduced the time required to extract information from our database of papers, with a total speedup of 25.25x compared to our initial implementation.
Processing the entirety of our database now takes less than 30 minutes using two A100 80-GB GPUs and 16 threads.
Compared to the traditional approach of manually reading and analyzing an entire article, which typically takes about one hour, this optimized workflow achieved a time savings of over 99% (Figure 3).
Figure 3. Required time for information extraction and categorization
In addition to information extraction, we also investigated automated article classification. By fine-tuning Llama-3.1-8b-Instruct with a LoRA adapter on a sample of papers manually annotated, we successfully automated the classification process, demonstrating its effectiveness in organizing complex datasets of scientific papers.
The results indicated that each article required only 2 seconds for classification, compared to the 300+ seconds required on average for a manual classification by an experienced reader (Figure 3).
Future directions
We’re currently refining our workflow to further accelerate the processing. We’re also improving our user interface to provide easy access to more locally deployed LLMs and enhance accessibility by other researchers (Figure 4).
We plan to implement the
NVIDIA AI Blueprint for multimodal PDF data extraction
to identify the most relevant articles for each research question and interact with those papers.
Beyond technical improvements, we are aiming to organize extracted key information for each question and generate visualizations (such as maps with location of the experiments mentioned in the papers) to accelerate further the writing of the systematic review.
Figure 4. Prototype interface undergoing in-house testing at SAEF
Summary
Our work at the Generative AI Codefest demonstrated the transformative potential of AI in accelerating systematic literature reviews. With NVIDIA NIM, we quickly moved from an idea to a working solution that significantly improves the process of information extraction from scientific papers.
This experience highlights how AI can streamline research workflows, enabling faster and more comprehensive insights. LLMs have the potential to facilitate interdisciplinary research, empowering scientists to explore complex, multi-domain research fields more effectively.
Moving forward, we aim to refine these methods and tools, ensuring that they are accessible and scalable for future research across diverse topics.
借助适用于 LLM 的 NVIDIA NIM 微服务加速科学文献审查
精心设计的系统审查通常是研究人员探索科学领域的第一步。对于刚刚接触该领域的科学家，它提供了该领域的结构化概述。对于专家而言，它可以细化他们的理解并激发新的想法。在 2024 年，
Web of Science
数据库收录了 218,650 篇评测文章，凸显了这些资源在研究中的重要性。
完成系统审查可显著增强研究人员的知识库及其学术影响。然而，传统的评论文章需要收集、阅读和总结有关特定主题的大量学术文章。由于这项人工练习非常耗时，处理过的文献的范围通常局限于几十篇或几百篇文章。跨学科内容 (通常超出研究人员的专业领域) 又增加了一层复杂性。
这些挑战使得创建全面、可靠且有影响力的系统审查变得越来越困难。
大语言模型 (LLMs) 的出现提供了一种突破性的解决方案，能够从大量文献中快速提取和合成信息。参加
Generative AI Codefest Australia
提供了一个独特的机会，让我们在 NVIDIA AI 专家的支持下探索这一想法，利用
NVIDIA NIM
微服务加速文献审查。这为我们的文献分析流程快速测试和微调了多个先进的 LLMs。
测试 LLMs 在处理论文方面的潜力
作为 ARC 特殊研究计划
(ARC Special Research Initiative Securing Antarctica’s Environmental Future (SAEF))
中的一个专门研究生理生态学的研究小组，我们着手撰写了一篇关于苔藓或地衣等非血管性植物对风的全球反应的文献回顾。
然而，我们很快就面临了一个挑战：许多关于风力电厂相互作用的相关文章都未能在标题或摘要中明确提及这些关键词，而在文献筛选过程中，这些关键词通常用作主要的过滤器。要对主题进行全面分析，需要手动阅读每篇文章的完整文本，这一过程非常耗时。
我们决定探索使用 LLMs 从文章中提取与风电场交互特别相关的内容的可能性。为此，我们
基于 LlaMa 3.1 8B Instruct NIM 微服务
实施了一个简单的问答应用（图 1）。这使我们能够快速获得初始原型。
第一个原型按顺序处理论文，在制作和优化提示以从每篇文章中提取关键信息方面非常有用。
图 1、以 LLM NIM 微服务为核心的初始简单论文处理流程
为了验证提取信息的准确性，我们首先手动验证结果。当测试数据集中未发现重大错误时，我们确定了使用 LLM 进一步提高关键信息提取效率的机会 (图 2)。其中包括将论文从 PDF 格式转换为结构化 JSON；提取图像、表格和图表；以及使用并行处理来加速论文处理速度。
图 2、增强型论文处理流程，整合 NV-ingest 和论文的并行处理
增强 LLMs 的性能，实现更高效的信息提取
通过将 NVIDIA NIM 微服务用于 LLM 和
nv-ingest
，我们在本地环境中部署了 LLM 和数据提取管道，并配备了 8 个 NVIDIA A100 80-GB GPU。我们还使用低等级适应性（LoRA）对模型进行了微调，以提高从论文中提取信息的准确性。
我们编译了一个数据集，其中包含与目标研究领域相关的 2000 多篇科学文章，这些文章均源自
Web of Science
和
Scopus
数据库。在 Generative AI Codefest 期间，我们在一周内专注于尝试各种策略，以优化从这些文章中提取关键信息的效率和准确性。
性能最佳的模型
为了确定性能最佳的模型，我们在一组随机选择的文章中测试了 NVIDIA API Catalog 中的一系列基于指令的和通用的 LLM。评估了每个模型在信息提取方面的准确性和全面性。
最终，我们确定 Llama-3.1-8B-instruct 最适合我们的需求。
处理速度
我们开发了一个问答模块，使用 Streamlit 回答用户定义的研究特定问题。
为进一步提高处理速度，我们对发送到 LLM 引擎的提示实施了并行处理，并使用了 KV 缓存，这使得使用 16 个线程时的计算时间显著缩短了 6 倍。
提取内容类型
我们使用 nv-ingest 将原始 PDF 中的内容 (包括文本、图形、表格和图表) 提取到结构化 JSON 文件中。这使得信息提取功能超越了文本内容，从而为回答问题提供更全面的环境。
在推理过程中使用 JSON 文件而非原始 PDF 文件也对将处理时间额外缩短 4.25 倍产生了重大影响。
成果
得益于这些改进，我们显著缩短了从论文数据库中提取信息所需的时间，与初始实施相比，总速度提高了 25.25 倍。
现在，使用两个 A100 80-GB GPU 和 16 个线程处理整个数据库只需不到 30 分钟。
与手动阅读和分析整篇文章的传统方法 (通常需要大约 1 小时) 相比，这种经过优化的工作流程可节省 99% 以上的时间 (图 3)。
图 3. 信息提取和分类所需的时间
除了信息提取，我们还研究了自动化文章分类。通过在手动标注的论文样本上使用 LoRA 适配器微调 Llama-3.1-8b-Instruct，我们成功实现了分类过程的自动化，证明了其在组织复杂的科学论文数据集方面的有效性。
结果表明，与经验丰富的读者手动分类平均所需的 300 多秒相比，每篇文章只需 2 秒即可完成分类 (图 3)。
未来方向
我们目前正在改进工作流程，以进一步加速处理。我们还在改进用户界面，以便轻松访问更多本地部署的 LLM，并增强其他研究人员的可访问性（图 4）。
我们计划实施用于多模态 PDF 数据提取的 NVIDIA AI Blueprint，以确定与每个研究问题最相关的文章，并与这些论文进行交互。
除了技术改进，我们的目标是组织提取的每个问题的关键信息，并生成可视化效果 (例如，包含论文中提到的实验位置的地图)，以进一步加速系统审查的编写。
图 4、原型接口正在 SAEF 进行内部测试
总结
我们在生成式 AI Codefest 上的工作展示了 AI 在加速系统文献审查方面的变革潜力。借助 NVIDIA NIM，我们迅速从想法转变为可行的解决方案，从而显著改善从科学论文中提取信息的过程。
此体验凸显了 AI 如何简化研究工作流程，从而更快获得更全面的见解。LLMs 具有促进跨学科研究的潜力，可助力科学家更有效地探索复杂的多领域研究领域。
展望未来，我们的目标是改进这些方法和工具，确保其可访问性和可扩展性，以供未来跨各种主题的研究使用。