让用户模糊的数据需求变成可迭代的明确查询
Demonstration of Pneuma-Seeker: Agentic System for Reifying and Fulfilling Information Needs on Tabular Data
- 将用户模糊问题转化为可检查的结构化数据请求
- 通过两个真实采购案例验证系统有效性
- 适合需要逐步明确分析目标的数据分析师
处理关系型数据时，数据分析师常从模糊或不完整的疑问出发，通过探索逐步完善问题。为支持这一迭代过程，我们展示了 Pneuma-Seeker 系统，该系统将用户的隐性信息需求显式化为可检查的、关系型的规范表达，实现信息需求的迭代优化、精准数据发现以及可追溯的执行。通过两个真实世界的采购案例，我们证明了 Pneuma-Seeker 如何利用大语言模型作为透明、交互式的分析协作者，而非黑箱答案引擎。
原文摘要 · Abstract (English)
Data analysts working with relational data often start with vague or underspecified questions and refine them iteratively as they explore the data. To support this iterative process, we demonstrate Pneuma-Seeker, a system that reifies a user's information need as explicit, inspectable relational specifications, enabling iterative refinement of the information need, targeted data discovery, and provenance-aware execution. Through two real-world procurement use cases, we show how Pneuma-Seeker leverages LLMs as transparent, interactive analytical collaborators rather than opaque answer engines.
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