@inproceedings{bansal-etal-2026-fragments,
title = "From Fragments to Facts: A Curriculum-Driven {DPO} Approach for Generating {H}indi News Veracity Explanations",
author = "Bansal, Pulkit and
Kumar, Raghvendra and
Singh, Shakti and
Jatowt, Adam and
Saha, Sriparna",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2108/",
pages = "42477--42497",
ISBN = "979-8-89176-395-1",
abstract = "In an era of rampant misinformation, generating reliable news explanations is vital, especially for underrepresented languages like Hindi. Lacking robust automated tools, Hindi faces challenges in scaling misinformation detection. To bridge this gap, we propose DeFactoX, a novel framework integrating Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. Fact-checked explanations from credible sources serve as preferred responses, while LLM outputs highlight system limitations and serve as non-preferred responses. At the core of this framework lies Hin-DPO, an enhanced variant of DPO that enriches the loss function with two novel parameters, Actuality and Finesse, enhancing explanation quality and consistency. Experiments with LLMs (Mistral, Llama, Gemma) and PLMs (mBART, mT5) confirm the framework{'}s effectiveness in generating coherent, contextually relevant explanations."
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<abstract>In an era of rampant misinformation, generating reliable news explanations is vital, especially for underrepresented languages like Hindi. Lacking robust automated tools, Hindi faces challenges in scaling misinformation detection. To bridge this gap, we propose DeFactoX, a novel framework integrating Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. Fact-checked explanations from credible sources serve as preferred responses, while LLM outputs highlight system limitations and serve as non-preferred responses. At the core of this framework lies Hin-DPO, an enhanced variant of DPO that enriches the loss function with two novel parameters, Actuality and Finesse, enhancing explanation quality and consistency. Experiments with LLMs (Mistral, Llama, Gemma) and PLMs (mBART, mT5) confirm the framework’s effectiveness in generating coherent, contextually relevant explanations.</abstract>
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%0 Conference Proceedings
%T From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations
%A Bansal, Pulkit
%A Kumar, Raghvendra
%A Singh, Shakti
%A Jatowt, Adam
%A Saha, Sriparna
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F bansal-etal-2026-fragments
%X In an era of rampant misinformation, generating reliable news explanations is vital, especially for underrepresented languages like Hindi. Lacking robust automated tools, Hindi faces challenges in scaling misinformation detection. To bridge this gap, we propose DeFactoX, a novel framework integrating Direct Preference Optimization (DPO) with Curriculum learning to align machine-generated explanations with human reasoning. Fact-checked explanations from credible sources serve as preferred responses, while LLM outputs highlight system limitations and serve as non-preferred responses. At the core of this framework lies Hin-DPO, an enhanced variant of DPO that enriches the loss function with two novel parameters, Actuality and Finesse, enhancing explanation quality and consistency. Experiments with LLMs (Mistral, Llama, Gemma) and PLMs (mBART, mT5) confirm the framework’s effectiveness in generating coherent, contextually relevant explanations.
%U https://aclanthology.org/2026.findings-acl.2108/
%P 42477-42497
Markdown (Informal)
[From Fragments to Facts: A Curriculum-Driven DPO Approach for Generating Hindi News Veracity Explanations](https://aclanthology.org/2026.findings-acl.2108/) (Bansal et al., Findings 2026)
ACL