An LLM-Based Approach for Insight Generation in Data Analysis

• URL: http://arxiv.org/abs/2503.11664v1
• Date: Thu, 20 Feb 2025 17:09:59 GMT
• Title: An LLM-Based Approach for Insight Generation in Data Analysis
• Authors: Alberto Sánchez Pérez, Alaa Boukhary, Paolo Papotti, Luis Castejón Lozano, Adam Elwood,
• Abstract summary: This paper introduces a novel approach using Large Language Models (LLMs) to automatically generate textual insights.<n>Given a multi-table database as input, our method leverages LLMs to produce concise, text-based insights that reflect interesting patterns in the tables.<n>The insights are evaluated for both correctness and subjective insightfulness using a hybrid model of human judgment and automated metrics.
• Score: 9.077654650104055
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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