Multihop Factual Claim Verification Using Natural Language Prompts
by Md Mezbaur Rahman, Mohammed Saidul Islam, Md Tahmid Rahman Laskar, Md Azam Hossain, and Abu Raihan Mostofa Kamal
Published onJun 05, 2023
Multihop Factual Claim Verification Using Natural Language Prompts
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Abstract
Claim verification based on factual evidence can become substantially difficult when the proof comprises several sentences, making it challenging for natural language processing (NLP) models to comprehend long-range dependencies. Inspired by the success of prompt learning in numerous NLP applications, in this paper, we introduce prompt learning for the multi-hop claim verification task. With extensive experiments, we achieve promising results using our proposed prompt-based method that leverages manually constructed prompts. By fine-tuning the language models with prompts, we have achieved an accuracy of 83.9% while obtaining an improved cross-domain generalization performance as well. Moreover, we conducted experiments in few-shot and zero-shot settings and obtained significantly better performance using prompt-based methods compared to traditional supervised learning techniques that are based on the fine-tuning paradigm to further demonstrate the effectiveness of prompt learning in claim verification.
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