ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification

• URL: http://arxiv.org/abs/2411.11247v1
• Date: Mon, 18 Nov 2024 02:35:15 GMT
• Title: ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification
• Authors: Son T. Luu, Hiep Nguyen, Trung Vo, Le-Minh Nguyen,
• Abstract summary: ZeFaV is a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models. We conducted empirical experiments to evaluate our approach on two multi-hop fact-checking datasets including HoVer and FEVEROUS.
• Score: 2.6874004806796523
• License:
• Abstract: In this paper, we propose ZeFaV - a zero-shot based fact-checking verification framework to enhance the performance on fact verification task of large language models by leveraging the in-context learning ability of large language models to extract the relations among the entities within a claim, re-organized the information from the evidence in a relationally logical form, and combine the above information with the original evidence to generate the context from which our fact-checking model provide verdicts for the input claims. We conducted empirical experiments to evaluate our approach on two multi-hop fact-checking datasets including HoVer and FEVEROUS, and achieved potential results results comparable to other state-of-the-art fact verification task methods.

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