@inproceedings{giacchetta-etal-2025-argumentative,
    title = "Argumentative Analysis of Legal Rulings: A Structured Framework Using Bobbitt{'}s Typology",
    author = "Giacchetta, Carlotta  and
      Bernardi, Raffaella  and
      Montini, Barbara  and
      Staiano, Jacopo  and
      Tomasi, Serena",
    editor = "Chistova, Elena  and
      Cimiano, Philipp  and
      Haddadan, Shohreh  and
      Lapesa, Gabriella  and
      Ruiz-Dolz, Ramon",
    booktitle = "Proceedings of the 12th Argument mining Workshop",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/display_plenaries/2025.argmining-1.10/",
    pages = "107--115",
    ISBN = "979-8-89176-258-9",
    abstract = "Legal reasoning remains one of the most complex and nuanced domains for AI, with current tools often lacking transparency and domain adaptability. While recent advances in large language models (LLMs) offer new opportunities for legal analysis, their ability to structure and interpret judicial argumentation remains unexplored. address this gap by proposing a structured framework for AI-assisted legal reasoning, centered on argumentative analysis. this work, we use GPT-4o for discourse-level and semantic analysis to identify argumentative units and classify them according to Philippe Bobbitt{'}s six constitutional modalities of legal reasoning.apply this framework to legal rulings from the Italian Court of Cassation.experimental findings indicate that LLM-based tools can effectively augment and streamline legal practice, by e.g. preprocessing the legal texts under scrutiny; still, the limited performance of the state-of-the-art generative model tested indicates significant room for progress in human-AI collaboration in the legal domain."
}