@inproceedings{tan-minh-etal-2024-enhancing,
    title = "Enhancing Legal Violation Identification with {LLM}s and Deep Learning Techniques: Achievements in the {L}egal{L}ens 2024 Competition",
    author = "Tan Minh, Nguyen  and
      Ngoc Mai, Duy  and
      Xuan Bach, Le  and
      Huu Dung, Nguyen  and
      Cong Minh, Pham  and
      Nguyen, Ha Thanh  and
      Vuong, Thi Hai Yen",
    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/jlcl-multiple-ingestion/2024.nllp-1.28/",
    doi = "10.18653/v1/2024.nllp-1.28",
    pages = "336--345",
    abstract = "LegalLens is a competition organized to encourage advancements in automatically detecting legal violations. This paper presents our solutions for two tasks Legal Named Entity Recognition (L-NER) and Legal Natural Language Inference (L-NLI). Our approach involves fine-tuning BERT-based models, designing methods based on data characteristics, and a novel prompting template for data augmentation using LLMs. As a result, we secured first place in L-NER and third place in L-NLI among thirty-six participants. We also perform error analysis to provide valuable insights and pave the way for future enhancements in legal NLP. Our implementation is available at https://github.com/lxbach10012004/legal-lens/tree/main"
}