@inproceedings{tran-etal-2024-deberta,
    title = "{D}e{BERT}a Beats Behemoths: A Comparative Analysis of Fine-Tuning, Prompting, and {PEFT} Approaches on {L}egal{L}ens{NER}",
    author = "Tran, Hanh Thi Hong  and
      Chatterjee, Nishan  and
      Pollak, Senja  and
      Doucet, Antoine",
    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/fix-sig-urls/2024.nllp-1.34/",
    doi = "10.18653/v1/2024.nllp-1.34",
    pages = "371--380",
    abstract = "This paper summarizes the participation of our team (Flawless Lawgic) in the legal named entity recognition (L-NER) task at LegalLens 2024: Detecting Legal Violations. Given possible unstructured texts (e.g., online media texts), we aim to identify legal violations by extracting legal entities such as ``violation'', ``violation by'', ``violation on'', and ``law''. This system-description paper discusses our approaches to address the task, empirically highlighting the performances of fine-tuning models from the Transformers family (e.g., RoBERTa and DeBERTa) against open-sourced LLMs (e.g., Llama, Mistral) with different tuning settings (e.g., LoRA, Supervised Fine-Tuning (SFT) and prompting strategies). Our best results, with a weighted F1 of 0.705 on the test set, show a 30 percentage points increase in F1 compared to the baseline and rank 2 on the leaderboard, leaving a marginal gap of only 0.4 percentage points lower than the top solution. Our solutions are available at github.com/honghanhh/lner."
}