Summary of Tables As Texts or Images: Evaluating the Table Reasoning Ability Of Llms and Mllms, by Naihao Deng et al.
Tables as Texts or Images: Evaluating the Table Reasoning Ability of LLMs and MLLMs
by Naihao Deng, Zhenjie Sun, Ruiqi He, Aman Sikka, Yulong Chen, Lin Ma, Yue Zhang, Rada Mihalcea
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates the capabilities of large language models (LLMs) in interpreting tabular data through various prompting strategies and data formats. The study assesses the effectiveness of five text-based and three image-based table representations, highlighting the impact of representation and prompting on LLM performance. The authors extend their analysis across six benchmarks for table-related tasks such as question-answering and fact-checking. By comparing different approaches, this research provides valuable insights into the effective use of LLMs for table-related tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well big language models can understand tables. Researchers tested different ways to ask questions and different types of tables to see what works best. They found that the type of table and how you ask a question both affect how well the model does. This study helps us know how to use these powerful models for things like answering questions about tables. |
Keywords
* Artificial intelligence * Prompting * Question answering
