PaperHelper: Knowledge-Based LLM QA Paper Reading Assistant

2025-02-20

AI Summary

To address the dual challenges of inefficient literature comprehension by researchers and hallucination-prone responses in LLM-based scholarly Q&A, this paper proposes a dual-enhanced retrieval-driven paper reading and question-answering assistant. Methodologically, it introduces a novel synergistic mechanism integrating RAFT re-ranking with RAG Fusion multi-path retrieval to significantly mitigate hallucination; pioneers Mermaid-based automated generation of structured document relationship graphs within the literature processing pipeline; and implements an end-to-end RAG framework built upon fine-tuned GPT-4 API, supporting batch PDF downloading, precise question answering, and interactive visualization. Experimental results demonstrate an F1 score of 60.04 and average response latency of 5.8 seconds—representing a 7% improvement over baseline RAG—achieving substantial advances in both knowledge extraction accuracy and interpretability.

Technology Category

• Natural Language Processing: Question Answering
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• Enhances researchers' literature browsing efficiency
• Reduces hallucinations in large language models
• Improves accuracy in knowledge extraction

Innovation

• Retrieval-Augmented Generation minimizes hallucinations
• RAFT and RAG Fusion enhance LLM performance
• Fine-tuned GPT-4 API achieves high F1 Score