用对话式工具高效探索海量文献，实现数据驱动的智能阅读。
Conversational Exploration of Literature Landscape with LitChat
- 构建对话式代理，自动理解问题并检索相关文献
- 通过知识图谱与数据挖掘生成有证据支持的洞察
- 适合需要快速掌握领域全貌的研究者和审稿人
我们正处在一个“大文献”时代，数字科学论文数量呈指数增长。传统人工阅读已难以应对，而现有大型语言模型（LLMs）受限于上下文窗口和幻觉问题，无法提供系统综述所需的全面、客观、开放与透明视角。本文提出 LitChat，一个端到端、交互式、对话式的文献探索代理，将 LLM 与数据驱动发现工具结合，可自动解析用户查询、检索相关文献、构建知识图谱，并运用多种数据挖掘技术生成基于证据的见解。通过在 AI4Health 领域的案例研究，验证了 LitChat 能够以数据为基础，快速引导用户穿越大规模文献图景，实现传统方法无法达成的高效探索。
原文摘要 · Abstract (English)
We are living in an era of "big literature", where the volume of digital scientific publications is growing exponentially. While offering new opportunities, this also poses challenges for understanding literature landscapes, as traditional manual reviewing is no longer feasible. Recent large language models (LLMs) have shown strong capabilities for literature comprehension, yet they are incapable of offering "comprehensive, objective, open and transparent" views desired by systematic reviews due to their limited context windows and trust issues like hallucinations. Here we present LitChat, an end-to-end, interactive and conversational literature agent that augments LLM agents with data-driven discovery tools to facilitate literature exploration. LitChat automatically interprets user queries, retrieves relevant sources, constructs knowledge graphs, and employs diverse data-mining techniques to generate evidence-based insights addressing user needs. We illustrate the effectiveness of LitChat via a case study on AI4Health, highlighting its capacity to quickly navigate the users through large-scale literature landscape with data-based evidence that is otherwise infeasible with traditional means.
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