@inproceedings{ali-etal-2026-argumentation,
    title = "Argumentation and Judgement Factors: {LLM}-based Discovery and Application in Insurance Disputes",
    author = "Ali, Basit  and
      Sinha, Anubhav  and
      Ramrakhiyani, Nitin  and
      Pawar, Sachin  and
      Palshikar, Girish Keshav  and
      Apte, Manoj",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.128/",
    pages = "2789--2804",
    ISBN = "979-8-89176-380-7",
    abstract = "In this work, we focus on discovery of legal factors for a specific case type under consideration (e.g., vehicle insurance disputes). We refer to these legal factors more explicitly as ``Argumentation and Judgement Factors'' (AJFs). AJFs encode specific legal knowledge that is important for legal argumentation and judicial decision making. We propose a multi-step approach for discovering a list of AJFs for a given case type using a set of relevant legal documents (e.g., past judgements, relevant acts) and Symbolic Knowledge Distillation (SKD) from a Large Language Model (LLM). We propose a novel geneRatE-CRitic-reviEW (RECREW) prompting strategy for effective SKD. We construct and evaluate the discovered list of AJFs on two different types of cases (auto-insurance and life insurance) and show their utility in a dispute resolution application."
}