@inproceedings{kumar-etal-2026-entailed,
title = "Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability",
author = "Kumar, Gaurav and
Garg, Ayush and
Mazumder, Debajyoti and
Kishore, Aditya and
Kumar, Babu and
Patro, Jasabanta",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.172/",
pages = "3431--3452",
ISBN = "979-8-89176-444-6",
abstract = "Automated fact-checking has been a challenging task for the research community. Prior work has explored 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 high enough for real-world deployment. We, on the other hand, propose a new learning paradigm, where evidence classification and entailed justifications made by generative language models (GLMs) are used to train encoder-only language models (ELMs). We conducted a rigorous set of experiments, comparing our approach with recent works along with various prompting and fine-tuning strategies. Additionally, we performed ablation studies, error analysis, quality analysis of model explanations, and a domain generalisation study to provide a comprehensive understanding of our approach."
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<abstract>Automated fact-checking has been a challenging task for the research community. Prior work has explored 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 high enough for real-world deployment. We, on the other hand, propose a new learning paradigm, where evidence classification and entailed justifications made by generative language models (GLMs) are used to train encoder-only language models (ELMs). We conducted a rigorous set of experiments, comparing our approach with recent works along with various prompting and fine-tuning strategies. Additionally, we performed ablation studies, error analysis, quality analysis of model explanations, and a domain generalisation study to provide a comprehensive understanding of our approach.</abstract>
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%0 Conference Proceedings
%T Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability
%A Kumar, Gaurav
%A Garg, Ayush
%A Mazumder, Debajyoti
%A Kishore, Aditya
%A Kumar, Babu
%A Patro, Jasabanta
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F kumar-etal-2026-entailed
%X Automated fact-checking has been a challenging task for the research community. Prior work has explored 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 high enough for real-world deployment. We, on the other hand, propose a new learning paradigm, where evidence classification and entailed justifications made by generative language models (GLMs) are used to train encoder-only language models (ELMs). We conducted a rigorous set of experiments, comparing our approach with recent works along with various prompting and fine-tuning strategies. Additionally, we performed ablation studies, error analysis, quality analysis of model explanations, and a domain generalisation study to provide a comprehensive understanding of our approach.
%U https://aclanthology.org/2026.findings-aacl.172/
%P 3431-3452
Markdown (Informal)
[Entailed Opinion Matters: Improving the Fact-Checking Performance of Language Models by Relying on their Entailment Ability](https://aclanthology.org/2026.findings-aacl.172/) (Kumar et al., Findings 2026)
ACL