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.
NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA
Applying fine-tuned BERT-based models and few-shot prompting on GPT models improves performance in legal argument reasoning for answer validation.
- Year
- 2024
- Venue
- arXiv 2024
- Authors
- 3
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- Abstract onlyARXIV-DEFAULT
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- arxiv.org/abs/2404.03150ARXIV-DEFAULT
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