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arXiv · 2502.14271

PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant

Congrui Yin·Evan Wei·Zhongxing Zhang·Zaifu Zhan
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Abstract

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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Congrui Yin, Evan Wei, Zhongxing Zhang, Zaifu Zhan. 2025-02-20. PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant. https://arxiv.org/abs/2502.14271

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