May 16, 2024, 4:46 a.m. | Majid Zarharan, Pascal Wullschleger, Babak Behkam Kia, Mohammad Taher Pilehvar, Jennifer Foster

cs.CL updates on

arXiv:2405.09454v1 Announce Type: new
Abstract: This paper presents a comprehensive analysis of explainable fact-checking through a series of experiments, focusing on the ability of large language models to verify public health claims and provide explanations or justifications for their veracity assessments. We examine the effectiveness of zero/few-shot prompting and parameter-efficient fine-tuning across various open and closed-source models, examining their performance in both isolated and joint tasks of veracity prediction and explanation generation. Importantly, we employ a dual evaluation approach comprising …

abstract analysis arxiv fact-checking few-shot health language language models large language large language models paper prompting public public health series through type verify

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