# An LLM-Based Approach for Insight Generation in Data Analysis

arXiv: https://arxiv.org/abs/2503.11664 · HTML: https://mlsanity.com/paper/2503.11664/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 35 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 14 of 14)

- [Reflexion: Language Agents with Verbal Reinforcement Learning](https://mlsanity.com/paper/2303.11366/) (2023)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [A General Language Assistant as a Laboratory for Alignment](https://mlsanity.com/paper/2112.00861/) (2021)
- [SnapKV: LLM Knows What You are Looking for Before Generation](https://mlsanity.com/paper/2404.14469/) (2024)
- [DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction](https://mlsanity.com/paper/2304.11015/) (2023)
- [Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation](https://mlsanity.com/paper/2308.15363/) (2023)
- [Exploring Chain-of-Thought Style Prompting for Text-to-SQL](https://mlsanity.com/paper/2305.14215/) (2023)
- [Elo Uncovered: Robustness and Best Practices in Language Model Evaluation](https://mlsanity.com/paper/2311.17295/) (2023)
- [SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)](https://mlsanity.com/paper/2306.00739/) (2023)
- [BEAVER: An Enterprise Benchmark for Text-to-SQL](https://mlsanity.com/paper/2409.02038/) (2024)
- [The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Survey](https://mlsanity.com/paper/2401.07872/) (2024)
- [Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System](https://mlsanity.com/paper/2304.00477/) (2023)
- [Characterizing Automated Data Insights](https://mlsanity.com/paper/2008.13060/) (2020)
- [DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databases](https://mlsanity.com/paper/2403.00872/) (2024)

## Built on by (most cited first, 12 of 12)

- [OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Value](https://mlsanity.com/paper/2512.14051/) (2025)
- [Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analytics](https://mlsanity.com/paper/2505.23695/) (2025)
- [DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytelling](https://mlsanity.com/paper/2604.06474/) (2026)
- [Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Models](https://mlsanity.com/paper/2602.02039/) (2026)
- [FAMOSE: A ReAct Approach to Automated Feature Discovery](https://mlsanity.com/paper/2602.17641/) (2026)
- [InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agents](https://mlsanity.com/paper/2511.22884/) (2025)
- [FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluation](https://mlsanity.com/paper/2510.13598/) (2025)
- [MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Data](https://mlsanity.com/paper/2512.13297/) (2025)
- [GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Tasks](https://mlsanity.com/paper/2608.01684/) (2026)
- [MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysis](https://mlsanity.com/paper/2508.01370/) (2025)
- [Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agent](https://mlsanity.com/paper/2605.07202/) (2026)
- [From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verification](https://mlsanity.com/paper/2609.23966/) (2026)

## Abstract (arXiv)

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.