  Fact-checking is an essential task in NLP that is commonly utilized for
validating the factual accuracy of claims. Prior work has mainly focused on
fine-tuning pre-trained languages models on specific datasets, which can be
computationally intensive and time-consuming. With the rapid development of
large language models (LLMs), such as ChatGPT and GPT-3, researchers are now
exploring their in-context learning capabilities for a wide range of tasks. In
this paper, we aim to assess the capacity of LLMs for fact-checking by
introducing Self-Checker, a framework comprising a set of plug-and-play modules
that facilitate fact-checking by purely prompting LLMs in an almost zero-shot
setting. This framework provides a fast and efficient way to construct
fact-checking systems in low-resource environments. Empirical results
demonstrate the potential of Self-Checker in utilizing LLMs for fact-checking.
However, there is still significant room for improvement compared to SOTA
fine-tuned models, which suggests that LLM adoption could be a promising
approach for future fact-checking research.
