ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910702797586432 |
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| author | Luu, Son T. Nguyen, Hiep Vo, Trung Nguyen, Le-Minh |
| author_facet | Luu, Son T. Nguyen, Hiep Vo, Trung Nguyen, Le-Minh |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2411_11247 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification Luu, Son T. Nguyen, Hiep Vo, Trung Nguyen, Le-Minh Computation and Language Artificial Intelligence 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. |
| title | ZeFaV: Boosting Large Language Models for Zero-shot Fact Verification |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2411.11247 |