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

Autor:	Luu, Son T., Nguyen, Hiep, Vo, Trung, Nguyen, Le-Minh
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
DOI:	10.1007/978-981-96-0119-6_28
Popis:	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. Comment: This pre-print has been published in PRICAI 2024: Trends in Artificial Intelligence. The published version is available at https://doi.org/10.1007/978-981-96-0119-6_28
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.11247 Zobrazit plný text záznamu View this record from Arxiv