Summary of Large Language Model For Table Processing: a Survey, by Weizheng Lu and Jing Zhang and Ju Fan and Zihao Fu and Yueguo Chen and Xiaoyong Du
Large Language Model for Table Processing: A Survey
by Weizheng Lu, Jing Zhang, Ju Fan, Zihao Fu, Yueguo Chen, Xiaoyong Du
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 survey provides a comprehensive overview of table-related tasks, examining both user scenarios and technical aspects. It covers traditional tasks like table question answering as well as emerging fields such as spreadsheet manipulation and table data analysis. The authors summarize the training techniques for Large Language Models (LLMs) and Visual Language Models (VLMs) tailored for table processing. Additionally, they discuss prompt engineering, particularly the use of LLM-powered agents, for various table-related tasks. Finally, they highlight several challenges, including diverse user input when serving and slow thinking using chain-of-thought. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers can help us with tables. Tables are important for storing and organizing data, and we need ways to work with them easily. The authors of this survey look at different tasks that involve working with tables, like answering questions about the information in a table or analyzing data from a spreadsheet. They also talk about how Large Language Models (LLMs) and Visual Language Models (VLMs) can be trained to help us with these tasks. The authors also discuss how we can use these models to make it easier for people to work with tables. |
Keywords
» Artificial intelligence » Prompt » Question answering
