用向量与图结构混合检索提升论文聊天机器人的查准率。
Enhancing Scientific Literature Chatbots with Retrieval-Augmented Generation: A Performance Evaluation of Vector and Graph-Based Systems
- 结合向量库与图数据库，实现文献与灰色文献的混合检索。
- 在单文档和大规模语料上均验证了检索准确率提升。
- 适合科研人员快速获取证据支持决策，尤其擅长复杂查询。
本文研究通过检索增强生成（RAG）技术提升科学文献聊天机器人性能，重点评估基于向量与图的检索系统。所提出的聊天机器人利用结构化（图）与非结构化（向量）数据库访问科学论文与灰色文献，根据研究目标高效筛选来源。为系统评估性能，我们设计两种使用场景：从单个上传文档检索与从大规模语料库检索。基准测试集由GPT模型生成，选取输出经人工标注用于评估。对比分析聚焦检索准确率与回复相关性，揭示两类方法的优劣。结果表明，混合RAG系统具备提升科学知识可及性的潜力，有助于支持基于证据的决策。
原文摘要 · Abstract (English)
This paper investigates the enhancement of scientific literature chatbots through retrieval-augmented generation (RAG), with a focus on evaluating vector- and graph-based retrieval systems. The proposed chatbot leverages both structured (graph) and unstructured (vector) databases to access scientific articles and gray literature, enabling efficient triage of sources according to research objectives. To systematically assess performance, we examine two use-case scenarios: retrieval from a single uploaded document and retrieval from a large-scale corpus. Benchmark test sets were generated using a GPT model, with selected outputs annotated for evaluation. The comparative analysis emphasizes retrieval accuracy and response relevance, providing insight into the strengths and limitations of each approach. The findings demonstrate the potential of hybrid RAG systems to improve accessibility to scientific knowledge and to support evidence-based decision making.
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