FootprintRAG：基于RAG的科学文献探索中证据上下文精化的可视化分析

中文摘要 AI 辅助

检索增强生成（RAG）越来越多地被用于将大语言模型（LLM）的输出锚定在科学文献中。然而，在开放式文献探索中，用于生成的证据上下文通常通过隐藏的检索、重排序、评估和过滤步骤产生。用户可能会收到检索摘要，却不知道系统如何构建证据上下文，哪些证据单元被保留或丢弃，或者是否有潜在有用的证据在合成前被排除。我们提出了FootprintRAG，一个由LLM智能体驱动的可视化分析系统，用于基于RAG的科学文献探索中的证据上下文精化。核心思想是将RAG证据上下文视为一个显式的、可检查的、可修改的分析对象，然后再进行生成。FootprintRAG将科学文献解析为文本和图形证据单元，将初始查询扩展为并行的查询变体，在迭代轮次中检索和评估证据，并从语料级证据空间中浮现出ERS排序的补充候选。通过协调视图，系统将检索轨迹、证据状态修订和具有溯源意识的摘要生成连接成一个用户可引导的工作流。我们通过两个案例研究、一项用户研究和与代表性RAG系统的工作流级比较来评估FootprintRAG。结果表明，FootprintRAG帮助用户比较检索方向、修订候选证据、恢复可能被忽视的证据，并将生成的摘要追溯到支持证据单元。FootprintRAG可在以下网址获取：此https URL。

英文摘要

Retrieval-Augmented Generation (RAG) is increasingly used to ground large language model (LLM) outputs in scientific literature. However, in open-ended literature exploration, the evidence context used for generation is often produced through hidden retrieval, reranking, assessment, and filtering steps. Users may receive retrieval summaries without knowing how the system constructed the evidence context, which evidence units were retained or discarded, or whether potentially useful evidence was excluded before synthesis. We present FootprintRAG, an LLM-agent-powered visual analytics system for evidence context refinement in RAG-based scientific literature exploration. The core idea is to treat the RAG evidence context as an explicit, inspectable, and revisable analytical object before generation. FootprintRAG parses scientific literature into text and figure evidence units, expands an initial query into parallel query variants, retrieves and assesses evidence across iterative rounds, and surfaces ERS-ranked supplementary candidates from the corpus-level evidence space. Through coordinated views, the system connects retrieval trajectories, evidence-state revision, and provenance-aware summary generation into a user-steerable workflow. We evaluate FootprintRAG through two case studies, a user study, and a workflow-level comparison with representative RAG systems. The results show that FootprintRAG helps users compare retrieval directions, revise candidate evidence, recover potentially overlooked evidence, and trace generated summaries back to supporting evidence units. FootprintRAG is available at https://github.com/meteorshowering/FootprintRAGVA.git.

发表机构

• Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
• University of Chinese Academy of Sciences（中国科学院大学）
• Hangzhou Institute for Advanced Study, UCAS（中国科学院杭州高等研究院）
• University of Technology Sydney（悉尼科技大学）
• Western Sydney University（西悉尼大学）

机构由 AI 辅助整理，请以论文原文为准。