用大模型和检索增强生成，自动从论文PDF生成文献综述。
Automated Literature Review Using NLP Techniques and LLM-Based Retrieval-Augmented Generation
- 结合NLP与大模型检索增强生成，实现自动综述
- GPT-3.5-turbo在ROUGE-1上达0.364，最优
- 适合需要快速梳理文献的研究者使用
本研究对比多种自然语言处理技术与基于大语言模型的检索增强生成（RAG）方法，实现自动化文献综述生成。面对科研论文数量激增带来的手动综述难题，本研究旨在仅通过PDF文件输入生成综述。选用SciTLDR数据集，采用三种方法构建系统：基于词频的spaCy、Transformer模型Simple T5，以及结合GPT-3.5-turbo的RAG。通过ROUGE指标评估，GPT-3.5-turbo在ROUGE-1上取得0.364最高分，Simple T5次之，spaCy最差。最终为最优系统开发了图形化界面。
原文摘要 · Abstract (English)
This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research articles provides a huge challenge for manual literature review. It has resulted in an increased demand for automation. Developing a system capable of automatically generating the literature reviews from only the PDF files as input is the primary objective of this research work. The effectiveness of several Natural Language Processing (NLP) strategies, such as the frequency-based method (spaCy), the transformer model (Simple T5), and retrieval-augmented generation (RAG) with Large Language Model (GPT-3.5-turbo), is evaluated to meet the primary objective. The SciTLDR dataset is chosen for this research experiment and three distinct techniques are utilized to implement three different systems for auto-generating the literature reviews. The ROUGE scores are used for the evaluation of all three systems. Based on the evaluation, the Large Language Model GPT-3.5-turbo achieved the highest ROUGE-1 score, 0.364. The transformer model comes in second place and spaCy is at the last position. Finally, a graphical user interface is created for the best system based on the large language model.
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