mlsanityData to 14 Oct 2026

An LLM-Based Approach for Insight Generation in Data Analysis

arXiv 2503.11664 ↗First posted 20 Feb 2025cs.AI cs.CL cs.DBcited by 12

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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.

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

14 of 35 references.

References · 14

  1. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,276
  2. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  3. 2021A General Language Assistant as a Laboratory for Alignmentcited by 1,055
  4. 2024SnapKV: LLM Knows What You are Looking for Before Generationcited by 880
  5. 2023DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correctioncited by 477
  6. 2023Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluationcited by 385
  7. 2023Exploring Chain-of-Thought Style Prompting for Text-to-SQLcited by 69
  8. 2023Elo Uncovered: Robustness and Best Practices in Language Model Evaluationcited by 61
  9. 2023SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended)cited by 42
  10. 2024BEAVER: An Enterprise Benchmark for Text-to-SQLcited by 42
  11. 2024The What, Why, and How of Context Length Extension Techniques in Large Language Models -- A Detailed Surveycited by 33
  12. 2023Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration Systemcited by 20
2 more
  1. 2020Characterizing Automated Data Insightscited by 12
  2. 2024DFIN-SQL: Integrating Focused Schema with DIN-SQL for Superior Accuracy in Large-Scale Databasescited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 6; 2026: 6.

12 papers.

  1. 2025OpenDataArena: A Fair and Open Arena for Benchmarking Post-Training Dataset Valuecited by 15
  2. 2025Data-to-Dashboard: Multi-Agent LLM Framework for Insightful Visualization in Enterprise Analyticscited by 11
  3. 2026DataSTORM: Deep Research on Large-Scale Databases using Exploratory Data Analysis and Data Storytellingcited by 7
  4. 2026Hunt Instead of Wait: Evaluating Deep Data Research on Large Language Modelscited by 5
  5. 2026FAMOSE: A ReAct Approach to Automated Feature Discoverycited by 4
  6. 2025InsightEval: An Expert-Curated Benchmark for Assessing Insight Discovery in LLM-Driven Data Agentscited by 3
  7. 2025FreshTab: Sourcing Fresh Data for Table-to-Text Generation Evaluationcited by 2
  8. 2025MedInsightBench: Evaluating Medical Analytics Agents Through Multi-Step Insight Discovery in Multimodal Medical Datacited by 2
  9. 2026GABench: A Comprehensive Benchmark for Evaluating LLM Agents on Graph Analysis Taskscited by 2
  10. 2025MaRGen: Multi-Agent LLM Approach for Self-Directed Market Research and Analysiscited by 0
  11. 2026Towards Autonomous Business Intelligence via Data-to-Insight Discovery Agentcited by 0
  12. 2026From Tables to Quantified Statements: Evaluating LLM Inference Generation through Executable Verificationcited by 0