# Towards Automated Cross-domain Exploratory Data Analysis through Large Language Models

arXiv: https://arxiv.org/abs/2412.07214 · HTML: https://mlsanity.com/paper/2412.07214/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 81 references that are in the map.
- First posted on arXiv 10 Dec 2024.

## Builds on (most cited first, 27 of 27)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://mlsanity.com/paper/1809.08887/) (2018)
- [Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs](https://mlsanity.com/paper/2305.03111/) (2023)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [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)
- [RESDSQL: Decoupling Schema Linking and Skeleton Parsing for Text-to-SQL](https://mlsanity.com/paper/2302.05965/) (2023)
- [CodeS: Towards Building Open-source Language Models for Text-to-SQL](https://mlsanity.com/paper/2402.16347/) (2024)
- [C3: Zero-shot Text-to-SQL with ChatGPT](https://mlsanity.com/paper/2307.07306/) (2023)
- [MAC-SQL: A Multi-Agent Collaborative Framework for Text-to-SQL](https://mlsanity.com/paper/2312.11242/) (2023)
- [SQLNet: Generating Structured Queries From Natural Language Without Reinforcement Learning](https://mlsanity.com/paper/1711.04436/) (2017)
- [Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing](https://mlsanity.com/paper/2012.12627/) (2020)
- [Graphix-T5: Mixing Pre-Trained Transformers with Graph-Aware Layers for Text-to-SQL Parsing](https://mlsanity.com/paper/2301.07507/) (2023)
- [ReAcTable: Enhancing ReAct for Table Question Answering](https://mlsanity.com/paper/2310.00815/) (2023)
- [Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing](https://mlsanity.com/paper/1905.06241/) (2019)
- [RYANSQL: Recursively Applying Sketch-based Slot Fillings for Complex Text-to-SQL in Cross-Domain Databases](https://mlsanity.com/paper/2004.03125/) (2020)
- [Before Generation, Align it! A Novel and Effective Strategy for Mitigating Hallucinations in Text-to-SQL Generation](https://mlsanity.com/paper/2405.15307/) (2024)
- [FinSQL: Model-Agnostic LLMs-based Text-to-SQL Framework for Financial Analysis](https://mlsanity.com/paper/2401.10506/) (2024)
- [ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser](https://mlsanity.com/paper/2104.04689/) (2021)
- [AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization](https://mlsanity.com/paper/2102.01330/) (2021)
- [Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows](https://mlsanity.com/paper/2105.00121/) (2021)
- [XInsight: eXplainable Data Analysis Through The Lens of Causality](https://mlsanity.com/paper/2207.12718/) (2022)
- [Duoquest: A Dual-Specification System for Expressive SQL Queries](https://mlsanity.com/paper/2003.07438/) (2020)
- [DataPrep.EDA: Task-Centric Exploratory Data Analysis for Statistical Modeling in Python](https://mlsanity.com/paper/2104.00841/) (2021)
- [Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity](https://mlsanity.com/paper/1906.10322/) (2019)
- [FEDEX: An Explainability Framework for Data Exploration Steps](https://mlsanity.com/paper/2209.06260/) (2022)

## Built on by (most cited first, 2 of 2)

- [Research on Violent Text Detection System Based on BERT-fasttext Model](https://mlsanity.com/paper/2412.16455/) (2024)
- [WeaveData: A Multimodal Data Analysis System with Self-Critiquing and Self-Evolving LLM Plans](https://mlsanity.com/paper/2609.34764/) (2026)

## Abstract (arXiv)

Exploratory data analysis (EDA), coupled with SQL, is essential for data analysts involved in data exploration and analysis. However, data analysts often encounter two primary challenges: (1) the need to craft SQL queries skillfully, and (2) the requirement to generate suitable visualization types that enhance the interpretation of query results. Due to its significance, substantial research efforts have been made to explore different approaches to address these challenges, including leveraging large language models (LLMs). However, existing methods fail to meet real-world data exploration requirements primarily due to (1) complex database schema; (2) unclear user intent; (3) limited cross-domain generalization capability; and (4) insufficient end-to-end text-to-visualization capability. This paper presents TiInsight, an automated SQL-based cross-domain exploratory data analysis system. First, we propose hierarchical data context (i.e., HDC), which leverages LLMs to summarize the contexts related to the database schema, which is crucial for open-world EDA systems to generalize across data domains. Second, the EDA system is divided into four components (i.e., stages): HDC generation, question clarification and decomposition, text-to-SQL generation (i.e., TiSQL), and data visualization (i.e., TiChart). Finally, we implemented an end-to-end EDA system with a user-friendly GUI interface in the production environment at PingCAP. We have also open-sourced all APIs of TiInsight to facilitate research within the EDA community. Through extensive evaluations by a real-world user study, we demonstrate that TiInsight offers remarkable performance compared to human experts. Specifically, TiSQL achieves an execution accuracy of 86.3% on the Spider dataset using GPT-4. It also demonstrates state-of-the-art performance on the Bird dataset.