Reasoning for fact verification using language models

dc.contributor.advisorGhorbani, Ali A.
dc.contributor.authorKanaani, Mohammadamin
dc.date.accessioned2024-05-08T19:55:17Z
dc.date.available2024-05-08T19:55:17Z
dc.date.issued2024-02
dc.description.abstractIn response to the proliferation of misinformation on social media platforms, this thesis introduces the Triple-R framework (Retriever, Ranker, Reasoner) to enhance fact-checking by leveraging the Web for evidence retrieval and generating understandable explanations for its decisions. Unlike existing methods, Triple-R incorporates external sources for evidence and provides explanations for datasets lacking them. By fine-tuning a causal language model, it produces natural language explanations and labels for evidence-claim pairs, aiming for greater transparency and interpretability in fact-checking systems. Evaluated on a popular dataset, Triple-R achieved a state-of-the-art accuracy of 42.72% on the LIAR benchmark, outperforming current automated fact verification methods. This underscores its effectiveness in integrating web sources and offering clear reasons, presenting a significant step forward in the fight against online misinformation.
dc.description.copyright© Mohammadamin Kanaani, 2024
dc.format.extentxviii, 139
dc.format.mediumelectronic
dc.identifier.oclc(OCoLC)1441292272en
dc.identifier.otherThesis 11367en
dc.identifier.urihttps://unbscholar.lib.unb.ca/handle/1882/37806
dc.language.isoen
dc.publisherUniversity of New Brunswick
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.subject.disciplineComputer Science
dc.subject.lcshMisinformation.en
dc.subject.lcshSocial media.en
dc.subject.lcshCausal relations (Linguistics)en
dc.titleReasoning for fact verification using language models
dc.typemaster thesis
oaire.license.conditionother
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of New Brunswick
thesis.degree.levelmasters
thesis.degree.nameM.C.S.

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