@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://preview.aclanthology.org/ingest-acl/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."
}