Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries
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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.
Key facts. Cited by 38 papers in the map (data through 7 Oct 2026). Builds on 10 of its 23 references that are in the map. First posted on arXiv 21 Dec 2023.
Builds on
10 of 23 references.
References · 10
- 2023StarCoder: may the source be with you!cited by 790
- 2017Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learningcited by 679
- 2020TAPAS: Weakly Supervised Table Parsing via Pre-trainingcited by 432
- 2021TAPEX: Table Pre-training via Learning a Neural SQL Executorcited by 233
- 2023Large Language Models are Versatile Decomposers: Decompose Evidence and Questions for Table-based Reasoningcited by 177
- 2016Language to Logical Form with Neural Attentioncited by 158
- 2023CodeGen2: Lessons for Training LLMs on Programming and Natural Languagescited by 124
- 2023Demonstration of InsightPilot: An LLM-Empowered Automated Data Exploration Systemcited by 20
- 2020Table2Charts: Recommending Charts by Learning Shared Table Representationscited by 14
- 2021A flexible forecasting model for production systemscited by 2
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Citing papers per year: 2024: 18; 2025: 15; 2026: 5.
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