# WikiTableEdit: A Benchmark for Table Editing by Natural Language Instruction

arXiv: https://arxiv.org/abs/2403.02962 · HTML: https://mlsanity.com/paper/2403.02962/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 13 references that are in the map.
- First posted on arXiv 5 Mar 2024.

## Builds on (most cited first, 8 of 8)

- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning](https://mlsanity.com/paper/1709.00103/) (2017)
- [Compositional Semantic Parsing on Semi-Structured Tables](https://mlsanity.com/paper/1508.00305/) (2015)
- [Challenges in Data-to-Document Generation](https://mlsanity.com/paper/1707.08052/) (2017)
- [Text2Analysis: A Benchmark of Table Question Answering with Advanced Data Analysis and Unclear Queries](https://mlsanity.com/paper/2312.13671/) (2023)
- [What to talk about and how? Selective Generation using LSTMs with Coarse-to-Fine Alignment](https://mlsanity.com/paper/1509.00838/) (2015)
- [WikiTableT: A Large-Scale Data-to-Text Dataset for Generating Wikipedia Article Sections](https://mlsanity.com/paper/2012.14919/) (2020)
- [InstructExcel: A Benchmark for Natural Language Instruction in Excel](https://mlsanity.com/paper/2310.14495/) (2023)

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

- [MiMoTable: A Multi-scale Spreadsheet Benchmark with Meta Operations for Table Reasoning](https://mlsanity.com/paper/2412.11711/) (2024)
- [LLMs Corrupt Your Documents When You Delegate](https://mlsanity.com/paper/2604.15597/) (2026)

## Abstract (arXiv)

Tabular data, as a crucial form of data representation, exists in diverse formats on the Web. When confronted with complex and irregular tables, manual modification becomes a laborious task. This paper investigates the performance of Large Language Models (LLMs) in the context of table editing tasks. Existing research mainly focuses on regular-shaped tables, wherein instructions are used to generate code in SQL, Python, or Excel Office-script for manipulating the tables. Nevertheless, editing tables with irregular structures, particularly those containing merged cells spanning multiple rows, poses a challenge when using code. To address this, we introduce the WikiTableEdit dataset. Leveraging 26,531 tables from the WikiSQL dataset, we automatically generate natural language instructions for six distinct basic operations and the corresponding outcomes, resulting in over 200,000 instances. Subsequently, we evaluate several representative large language models on the WikiTableEdit dataset to demonstrate the challenge of this task. The dataset will be released to the community to promote related researches.