Exploring data is crucial in data analysis, as it helps users understand and
interpret the data more effectively. However, performing effective data
exploration requires in-depth knowledge of the dataset and expertise in data
analysis techniques. Not being familiar with either can create obstacles that
make the process time-consuming and overwhelming for data analysts. To address
this issue, we introduce InsightPilot, an LLM (Large Language Model)-based,
automated data exploration system designed to simplify the data exploration
process. InsightPilot automatically selects appropriate analysis intents, such
as understanding, summarizing, and explaining. Then, these analysis intents are
concretized by issuing corresponding intentional queries (IQueries) to create a
meaningful and coherent exploration sequence. In brief, an IQuery is an
abstraction and automation of data analysis operations, which mimics the
approach of data analysts and simplifies the exploration process for users. By
employing an LLM to iteratively collaborate with a state-of-the-art insight
engine via IQueries, InsightPilot is effective in analyzing real-world
datasets, enabling users to gain valuable insights through natural language
inquiries. We demonstrate the effectiveness of InsightPilot in a case study,
showing how it can help users gain valuable insights from their datasets.

本文介绍 InsightPilot，这是一个基于 LLM（Large Language Model）的自动化数据探索系统，可以通过发出对应的意图查询（IQueries）来简化数据探索流程，通过其与最先进的洞察引擎之间的迭代协作，有效地分析现实世界的数据集，使用户可以通过自然语言查询获得有价值的见解。