发表机构
University of Washington（华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出DAG-EDA系统，通过意图图与多层知识图谱辅助分析人员结合LLM开展探索性数据分析，解决LLM输出非结构化的问题，提升分析推理与导航能力。
AI 中文摘要
探索性数据分析（EDA）在实践中很少是无限制的：分析人员从高级领域问题出发，逐步推进到能够回答这些问题的具体分析，利用领域知识和先验假设来确定分析方向。大型语言模型（LLM）可以提供这类知识，但其输出是非结构化的，导致分析人员无法查看已探索的内容、缺失的部分，或某一分析方向被选中的原因。我们提出DAG-EDA系统，该系统通过两个关联结构让分析人员与LLM共同导航可能的分析空间：受分析意图语法约束的意图图，将模糊的自然语言问题分解为逐步具体的分析任务，保留替代框架，允许分析人员分支、回溯和比较路径；多层知识图谱将LLM的领域知识外部化，连接领域概念与可测量这些概念的数据集变量，使分析人员能够检查和质疑其问题如何基于数据。两个图谱仅由数据集和分析人员的问题构建，分析人员得出的分析结果以交互式仪表板呈现。我们通过使用场景说明该系统，并描述一项用户研究设计，以检验该系统是否能辅助分析人员的推理与导航。
英文摘要
Exploratory data analysis (EDA) is rarely open-ended in practice: analysts work from high-level domain questions toward the concrete analyses that can answer them, prioritizing directions with domain knowledge and prior hypotheses. Large language models (LLMs) can supply such knowledge, but their responses are unstructured, leaving analysts no way to see what has been explored, what is missing, or why one direction was chosen over another. We present DAG-EDA, a system that lets analysts and an LLM co-navigate the space of possible analyses through two linked structures. An intent graph, governed by a grammar of analytical intent, decomposes an ambiguous natural-language question into progressively concrete analysis tasks, keeping alternative framings open and letting analysts branch, backtrack, and compare paths. A multi-layered knowledge graph externalizes the LLM's domain knowledge, linking domain concepts to the dataset variables that can measure them, so analysts can inspect and contest how their question is grounded in the data. Both graphs are constructed from only the dataset and the analyst's question, and the analyses the analyst reaches are rendered as interactive dashboards. We illustrate the system through a usage scenario and describe a user study design for examining whether the system scaffold analysts' reasoning and navigation.
