Summary of Are Large Language Models Table-based Fact-checkers?, by Hanwen Zhang et al.
Are Large Language Models Table-based Fact-Checkers?
by Hanwen Zhang, Qingyi Si, Peng Fu, Zheng Lin, Weiping Wang
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 paper explores the potential of Large Language Models (LLMs) in Table-based Fact Verification (TFV), a task that involves extracting the entailment relation between statements and structured tables. The authors design various prompts to investigate how LLMs can perform zero-shot and few-shot TFV with in-context learning. They also examine the effect of instruction tuning on LLMs’ performance, finding significant improvements in TFV capability. The results show that LLMs can achieve acceptable results in zero-shot and few-shot TFV scenarios, especially when prompted correctly. Furthermore, the authors discuss possible directions to enhance the accuracy of TFV via LLMs, paving the way for future research on table reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how big language models can help check facts from tables. Right now, smaller models don’t do very well with this task because they need more practice data and aren’t good at learning new things without being trained first. But bigger models, called Large Language Models (LLMs), are really good at learning new things on their own. The researchers wanted to see if LLMs could be used for fact-checking from tables. They tried different ways of asking the model questions and found that it got better at checking facts when given a little practice first. This study shows that LLMs can be useful for checking facts from tables, especially if you give them some guidance. It also gives us ideas about how to make LLMs even better at this task. |
Keywords
* Artificial intelligence * Few shot * Instruction tuning * Zero shot
