Self-Checker: Plug-and-Play Modules for Fact-Checking with Large Language Models
This addresses fact-checking in NLP for low-resource environments, offering a faster alternative to fine-tuning, though it is incremental as it builds on existing in-context learning capabilities.
The paper tackled fact-checking by introducing Self-Checker, a plug-and-play framework that uses prompting with large language models in a nearly zero-shot setting, showing potential but with significant room for improvement compared to state-of-the-art fine-tuned models.
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.