Summary of Wikitableedit: a Benchmark For Table Editing by Natural Language Instruction, By Zheng Li and Xiang Chen and Xiaojun Wan
WikiTableEdit: A Benchmark for Table Editing by Natural Language Instruction
by Zheng Li, Xiang Chen, Xiaojun Wan
First submitted to arxiv on: 5 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the capabilities of Large Language Models (LLMs) in table editing tasks, particularly with complex and irregular tables containing merged cells. Current approaches focus on regular-shaped tables using code manipulation through SQL, Python, or Excel. However, this is insufficient for handling irregular structures. The authors introduce the WikiTableEdit dataset, automatically generating natural language instructions for six basic operations (e.g., filtering, sorting) based on 26,531 tables from the WikiSQL dataset. They evaluate several LLMs on this dataset to demonstrate the challenge of this task. This research aims to promote related studies and advance the field of table editing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to edit a big spreadsheet with lots of complicated formulas and merged cells. It’s a tough job! Most people use code to make changes, but what if you don’t know how to write code or need to make changes quickly? This paper looks at using special language models (like the ones that can understand natural language) to help edit tables. The researchers created a big dataset of over 200,000 table editing tasks and tested several different language models on it. They want to help others do similar work and advance our understanding of how to make changes to complex spreadsheets. |
