Improving the fact-checking performance of language models by relying on their entailment ability

• URL: http://arxiv.org/abs/2505.15050v3
• Date: Tue, 21 Oct 2025 17:05:38 GMT
• Title: Improving the fact-checking performance of language models by relying on their entailment ability
• Authors: Gaurav Kumar, Debajyoti Mazumder, Ayush Garg, Jasabanta Patro,
• Abstract summary: We propose a simple yet effective strategy to train encoder-only language models (ELMs) for fact-checking.<n>We conducted a rigorous set of experiments, comparing our approach with recent works and various prompting and fine-tuning strategies to demonstrate the superiority of our approach.
• Score: 3.371541812350348
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Automated fact-checking has been a challenging task for the research community. Past works tried various strategies, such as end-to-end training, retrieval-augmented generation, and prompt engineering, to build robust fact-checking systems. However, their accuracy has not been very high for real-world deployment. We, on the other hand, propose a simple yet effective strategy, where entailed justifications generated by LLMs are used to train encoder-only language models (ELMs) for fact-checking. We conducted a rigorous set of experiments, comparing our approach with recent works and various prompting and fine-tuning strategies to demonstrate the superiority of our approach. Additionally, we did quality analysis of model explanations, ablation studies, and error analysis to provide a comprehensive understanding of our approach.

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