# Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries

arXiv: https://arxiv.org/abs/2312.13671 · HTML: https://mlsanity.com/paper/2312.13671/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 23 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 10 of 10)

- [StarCoder: may the source be with you!](https://mlsanity.com/paper/2305.06161/) (2023)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [TAPAS: Weakly Supervised Table Parsing via Pre-training](https://mlsanity.com/paper/2004.02349/) (2020)
- [TAPEX: Table Pre-training via Learning a Neural SQL Executor](https://mlsanity.com/paper/2107.07653/) (2021)
- [Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoning](https://mlsanity.com/paper/2301.13808/) (2023)
- [Language to Logical Form with Neural Attention](https://mlsanity.com/paper/1601.01280/) (2016)
- [CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://mlsanity.com/paper/2305.02309/) (2023)
- [Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration System](https://mlsanity.com/paper/2304.00477/) (2023)
- [Table2Charts: Recommending Charts by Learning Shared Table Representations](https://mlsanity.com/paper/2008.11015/) (2020)
- [A flexible forecasting model for production systems](https://mlsanity.com/paper/2105.01098/) (2021)

## Built on by (most cited first, 30 of 38)

- [ChartX & ChartVLM: A Versatile Benchmark and Foundation Model for Complicated Chart Reasoning](https://mlsanity.com/paper/2402.12185/) (2024)
- [TableBench: A Comprehensive and Complex Benchmark for Table Question Answering](https://mlsanity.com/paper/2408.09174/) (2024)
- [SpreadsheetBench: Towards Challenging Real World Spreadsheet Manipulation](https://mlsanity.com/paper/2406.14991/) (2024)
- [DataSciBench: An LLM Agent Benchmark for Data Science](https://mlsanity.com/paper/2502.13897/) (2025)
- [WaitGPT: Monitoring and Steering Conversational LLM Agent in Data Analysis with On-the-Fly Code Visualization](https://mlsanity.com/paper/2408.01703/) (2024)
- [DocGenome: An Open Large-scale Scientific Document Benchmark for Training and Testing Multi-modal Large Language Models](https://mlsanity.com/paper/2406.11633/) (2024)
- [nvAgent: Automated Data Visualization from Natural Language via Collaborative Agent Workflow](https://mlsanity.com/paper/2502.05036/) (2025)
- [Large Language Model-based Data Science Agent: A Survey](https://mlsanity.com/paper/2508.02744/) (2025)
- [LightVA: Lightweight Visual Analytics with LLM Agent-Based Task Planning and Execution](https://mlsanity.com/paper/2411.05651/) (2024)
- [PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries](https://mlsanity.com/paper/2410.11076/) (2024)
- [nvBench 2.0: Resolving Ambiguity in Text-to-Visualization through Stepwise Reasoning](https://mlsanity.com/paper/2503.12880/) (2025)
- [Tapilot-Crossing: Benchmarking and Evolving LLMs Towards Interactive Data Analysis Agents](https://mlsanity.com/paper/2403.05307/) (2024)
- [SpreadsheetLLM: Encoding Spreadsheets for Large Language Models](https://mlsanity.com/paper/2407.09025/) (2024)
- [TQA-Bench: Evaluating LLMs for Multi-Table Question Answering](https://mlsanity.com/paper/2411.19504/) (2024)
- [RealHiTBench: A Comprehensive Realistic Hierarchical Table Benchmark for Evaluating LLM-Based Table Analysis](https://mlsanity.com/paper/2506.13405/) (2025)
- [TART: An Open-Source Tool-Augmented Framework for Explainable Table-based Reasoning](https://mlsanity.com/paper/2409.11724/) (2024)
- [Tabular Data Understanding with LLMs: A Survey of Recent Advances and Challenges](https://mlsanity.com/paper/2508.00217/) (2025)
- [CoCoST: Automatic Complex Code Generation with Online Searching and Correctness Testing](https://mlsanity.com/paper/2403.13583/) (2024)
- [DataCross: A Unified Benchmark and Agent Framework for Cross-Modal Heterogeneous Data Analysis](https://mlsanity.com/paper/2601.21403/) (2026)
- [LongDA: Benchmarking LLM Agents for Long-Document Data Analysis](https://mlsanity.com/paper/2601.02598/) (2026)
- [Automated Formalization via Conceptual Retrieval-Augmented LLMs](https://mlsanity.com/paper/2508.06931/) (2025)
- [T2R-bench: A Benchmark for Generating Article-Level Reports from Real World Industrial Tables](https://mlsanity.com/paper/2508.19813/) (2025)
- [Table Question Answering in the Era of Large Language Models: A Comprehensive Survey of Tasks, Methods, and Evaluation](https://mlsanity.com/paper/2510.09671/) (2025)
- [RETQA: A Large-Scale Open-Domain Tabular Question Answering Dataset for Real Estate Sector](https://mlsanity.com/paper/2412.10104/) (2024)
- [TableLoRA: Low-rank Adaptation on Table Structure Understanding for Large Language Models](https://mlsanity.com/paper/2503.04396/) (2025)
- [QUIS: Question-guided Insights Generation for Automated Exploratory Data Analysis](https://mlsanity.com/paper/2410.10270/) (2024)
- [Evaluating and Enhancing LLMs for Multi-turn Text-to-SQL with Multiple Question Types](https://mlsanity.com/paper/2412.17867/) (2024)
- [Large Language Models for Predictive Analysis: How Far Are They?](https://mlsanity.com/paper/2505.17149/) (2025)
- [Formula-R1: Incentivizing LLM Reasoning over Complex Tables with Numerical Computation via Formula-Driven Reinforcement Learning](https://mlsanity.com/paper/2505.23667/) (2025)
- [WikiTableEdit: A Benchmark for Table Editing by Natural Language Instruction](https://mlsanity.com/paper/2403.02962/) (2024)

## Abstract (arXiv)

Tabular data analysis is crucial in various fields, and large language models show promise in this area. However, current research mostly focuses on rudimentary tasks like Text2SQL and TableQA, neglecting advanced analysis like forecasting and chart generation. To address this gap, we developed the Text2Analysis benchmark, incorporating advanced analysis tasks that go beyond the SQL-compatible operations and require more in-depth analysis. We also develop five innovative and effective annotation methods, harnessing the capabilities of large language models to enhance data quality and quantity. Additionally, we include unclear queries that resemble real-world user questions to test how well models can understand and tackle such challenges. Finally, we collect 2249 query-result pairs with 347 tables. We evaluate five state-of-the-art models using three different metrics and the results show that our benchmark presents introduces considerable challenge in the field of tabular data analysis, paving the way for more advanced research opportunities.