@inproceedings{rajaraman-veeramani-2024-semantists,
    title = "Semantists at {L}egal{L}ens-2024: Data-efficient Training of {LLM}`s for Legal Violation Identification",
    author = "Rajaraman, Kanagasabai  and
      Veeramani, Hariram",
    editor = "Aletras, Nikolaos  and
      Chalkidis, Ilias  and
      Barrett, Leslie  and
      Goanț{\u{a}}, C{\u{a}}t{\u{a}}lina  and
      Preoțiuc-Pietro, Daniel  and
      Spanakis, Gerasimos",
    booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
    month = nov,
    year = "2024",
    address = "Miami, FL, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/Add-Cong-Liu-Florida-Atlantic-University-author-id/2024.nllp-1.31/",
    doi = "10.18653/v1/2024.nllp-1.31",
    pages = "355--360",
    abstract = "In this paper, we describe our system for LegalLens-2024 Shared Task on automatically identifying legal violations from unstructured text sources. We participate in Subtask B, called Legal Natural Language Inference (L-NLI), that aims to predict the relationship between a given premise summarizing a class action complaint and a hypothesis from an online media text, indicating any association between the review and the complaint. This task is challenging as it provides only limited labelled data. In our work, we adopt LLM based methods and explore various data-efficient learning approaches for maximizing performance. In the end, our best model employed an ensemble of LLM`s fine-tuned on the task-specific data, and achieved a Macro F1 score of 78.5{\%} on test data, and ranked 2nd among all teams submissions."
}