PaperHelper用RAG技术帮研究者高效准确读论文，减少大模型幻觉。
PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant
- 基于RAG框架与RAFT、RAG Fusion提升文献问答准确性。
- 在微调GPT-4上实现60.04的F1分数，延迟仅5.8秒。
- 支持批量下载和用Mermaid图展示论文间结构关系，适合科研人员。
本文介绍了一款名为PaperHelper的论文阅读助手，旨在提升研究人员高效浏览与理解科学文献的能力。该工具采用检索增强生成（RAG）框架，有效降低大语言模型（LLMs）常见的幻觉问题，优化高质量知识的提取。通过引入RAFT和RAG Fusion等先进技术，显著提升了基于LLM的文献综述在性能、准确性和可靠性方面的表现。此外，PaperHelper具备用户友好的界面，支持文档批量下载，并使用Mermaid格式可视化文献间的结构关系。实验结果表明，基于微调GPT-4 API的PaperHelper在测试中取得60.04的F1分数，延迟仅为5.8秒，相比基础RAG模型提升7%的F1得分。
原文摘要 · Abstract (English)
In the paper, we introduce a paper reading assistant, PaperHelper, a potent tool designed to enhance the capabilities of researchers in efficiently browsing and understanding scientific literature. Utilizing the Retrieval-Augmented Generation (RAG) framework, PaperHelper effectively minimizes hallucinations commonly encountered in large language models (LLMs), optimizing the extraction of accurate, high-quality knowledge. The implementation of advanced technologies such as RAFT and RAG Fusion significantly boosts the performance, accuracy, and reliability of the LLMs-based literature review process. Additionally, PaperHelper features a user-friendly interface that facilitates the batch downloading of documents and uses the Mermaid format to illustrate structural relationships between documents. Experimental results demonstrate that PaperHelper, based on a fine-tuned GPT-4 API, achieves an F1 Score of 60.04, with a latency of only 5.8 seconds, outperforming the basic RAG model by 7\% in F1 Score.
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