NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA
<|reference_start|>NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA: This paper presents our submission to the SemEval 2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. We present two approaches to solving the task of legal answer validation, given an introduction to the case, a question and an answer candidate. Firstly, we fine-tuned pre-trained BERT-based models and found that models trained on domain knowledge perform better. Secondly, we performed few-shot prompting on GPT models and found that reformulating the answer validation task to be a multiple-choice QA task remarkably improves the performance of the model. Our best submission is a BERT-based model that achieved the 7th place out of 20.<|reference_end|>
@article{pahilajani2024nlp,
    title={NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation
  using Few-Shot Multi-Choice QA},
    author={Anish Pahilajani, Samyak Rajesh Jain, Devasha Trivedi},
    journal={arXiv preprint arXiv:2404.03150},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.03150},
    primaryClass={cs.CL cs.AI}
}