Are Large Language Models Table-based Fact-Checkers?

Table-based Fact Verification (TFV) aims to extract the entailment relationship between statements and structured tables. Existing TFV methods based on small-scale models suffer from insufficient labeled data and weak zero-shot ability. Recently, the appearance of Large Language Models (LLMs) has gained lots of attraction in research fields. They have shown strong zero-shot and in-context learning capabilities on several NLP tasks, but their potential on TFV is still unknown. In this work, we implement a preliminary study on whether LLMs are table-based fact-checkers. In detail, we design various prompts to explore how in-context learning can help LLMs in TFV, i.e., zero-shot and few-shot TFV capability. Besides, we carefully design and construct TFV instructions to investigate the performance gain brought by the instruction tuning of LLMs. Experimental results demonstrate that LLMs can achieve acceptable results on zero-shot and few-shot TFV with prompt engineering, while instruction-tuning can stimulate the TFV capability significantly. We also make some valuable findings about the format of zero-shot prompts and the number of in-context examples. Finally, we analyze some possible directions to promote the accuracy of TFV via LLMs, which will benefit further research on table reasoning.