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arXiv · 2503.11664

An LLM-Based Approach for Insight Generation in Data Analysis

Alberto Sánchez Pérez·Alaa Boukhary·Paolo Papotti·Luis Castejón Lozano·Adam Elwood
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Abstract

Generating insightful and actionable information from databases is critical in data analysis. This paper introduces a novel approach using Large Language Models (LLMs) to automatically generate textual insights. Given a multi-table database as input, our method leverages LLMs to produce concise, text-based insights that reflect interesting patterns in the tables. Our framework includes a Hypothesis Generator to formulate domain-relevant questions, a Query Agent to answer such questions by generating SQL queries against a database, and a Summarization module to verbalize the insights. The insights are evaluated for both correctness and subjective insightfulness using a hybrid model of human judgment and automated metrics. Experimental results on public and enterprise databases demonstrate that our approach generates more insightful insights than other approaches while maintaining correctness.

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BibTeXRIS

Alberto Sánchez Pérez, Alaa Boukhary, Paolo Papotti, Luis Castejón Lozano, Adam Elwood. 2025-02-20. An LLM-Based Approach for Insight Generation in Data Analysis. https://arxiv.org/abs/2503.11664

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