@inproceedings{m-etal-2025-tutorial,
    title = "Tutorial on Trustworthy Legal Text Processing with {LLM}s: Retrieval, Rhetorical Roles, Summarization, and Trustworthy Generation",
    author = "M, Anand Kumar  and
      S, Sangeetha  and
      R, Manikandan  and
      R, Anjali",
    editor = "Heinzerling, Benjamin  and
      Ku, Lun-Wei",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics: Tutorial Abstract",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-tutorials.6/",
    pages = "34--39",
    ISBN = "979-8-89176-302-9",
    abstract = "This half-day tutorial provides a comprehensive overview of Legal Natural Language Processing (NLP) with LLM for participants with a basic understanding of Computational Linguistics or NLP concepts. We introduce how NLP can help analyze and manage legal text by covering five key topics: legal text analysis with LLM insights, legal text retrieval, rhetorical role identification, legal text summarization, and addressing bias and hallucination in legal tasks. Our goals are to explain why these tasks matter for researchers in the legal domain, describe the challenges and open problems, and outline current solutions. This proposed tutorial blends lectures, live examples, and Q{\&}A to help researchers and students see how language technology and LLMs can make legal information more understandable and efficient."
}