An Empirical Study of Using ChatGPT for Fact Verification Task

ChatGPT has recently emerged as a powerful tool for performing diverse NLP tasks. However, ChatGPT has been criticized for generating nonfactual responses, raising concerns about its usability for sensitive tasks like fact verification. This study investigates three key research questions: (1) Can ChatGPT be used for fact verification tasks? (2) What are different prompts performance using ChatGPT for fact verification tasks? (3) For the best-performing prompt, what common mistakes does ChatGPT make? Specifically, this study focuses on conducting a comprehensive and systematic analysis by designing and comparing the performance of three different prompts for fact verification tasks on the benchmark FEVER dataset using ChatGPT.

Zero-shot Approach to Overcome Perturbation Sensitivity of Prompts

Recent studies have demonstrated that natural-language prompts can help to leverage the knowledge learned by pre-trained language models for the binary sentence-level sentiment classification task. Specifically, these methods utilize few-shot learning settings to fine-tune the sentiment classification model using manual or automatically generated prompts. However, the performance of these methods is sensitive to the perturbations of the utilized prompts. Furthermore, these methods depend on a few labeled instances for automatic prompt generation and prompt ranking. This study aims to find high-quality prompts for the given task in a zero-shot setting. Given a base prompt, our proposed approach automatically generates multiple prompts similar to the base prompt employing positional, reasoning, and paraphrasing techniques and then ranks the prompts using a novel metric. We empirically demonstrate that the top-ranked prompts are high-quality and significantly outperform the base prompt and the prompts generated using few-shot learning for the binary sentence-level sentiment classification task.