An LLM-Based Approach for Insight Generation in Data Analysis

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.

@article{pérez2025_2503.11664,
  title={ An LLM-Based Approach for Insight Generation in Data Analysis },
  author={ Alberto Sánchez Pérez and Alaa Boukhary and Paolo Papotti and Luis Castejón Lozano and Adam Elwood },
  journal={arXiv preprint arXiv:2503.11664},
  year={ 2025 }
}