用AI助手自动检索多立场数据，帮用户增强论述可信度
DataScout: Automatic Data Fact Retrieval for Statement Augmentation with an LLM-Based Agent
- 基于大模型构建可交互的检索树，支持人机协同扩展
- 通过思维导图界面直观展示数据溯源路径，提升分析效率
- 适合需要快速验证观点、撰写深度报告的研究者与从业者
一篇数据叙事通常需整合多个视角和立场的数据事实，以构建全面客观的叙述。然而，获取这些事实需耗费大量时间进行数据搜寻，并对创作者的分析能力提出挑战。本文提出DataScout，一个交互式系统，能自动执行推理与立场导向的数据事实检索，以增强用户的陈述。DataScout利用基于大模型的智能体构建检索树，实现用户与智能体对检索过程的协同控制。界面将检索树可视化为思维导图，帮助用户直观引导检索方向，有效参与推理与分析。通过案例研究与深入专家访谈评估，结果表明DataScout能有效从不同立场检索多维度数据事实，帮助用户验证陈述并提升叙事可信度。
原文摘要 · Abstract (English)
A data story typically integrates data facts from multiple perspectives and stances to construct a comprehensive and objective narrative. However, retrieving these facts demands time for data search and challenges the creator's analytical skills. In this work, we introduce DataScout, an interactive system that automatically performs reasoning and stance-based data facts retrieval to augment the user's statement. Particularly, DataScout leverages an LLM-based agent to construct a retrieval tree, enabling collaborative control of its expansion between users and the agent. The interface visualizes the retrieval tree as a mind map that eases users to intuitively steer the retrieval direction and effectively engage in reasoning and analysis. We evaluate the proposed system through case studies and in-depth expert interviews. Our evaluation demonstrates that DataScout can effectively retrieve multifaceted data facts from different stances, helping users verify their statements and enhance the credibility of their stories.
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