NLP at UC Santa Cruz at SemEval-2024 Task 5: Legal Answer Validation using Few-Shot Multi-Choice QA
This addresses legal reasoning for NLP applications, but it is incremental as it adapts existing methods to a specific domain task.
The paper tackled legal answer validation by fine-tuning BERT models and using few-shot prompting with GPT, reformulating it as a multiple-choice QA task, with the best model achieving 7th place out of 20 submissions.
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.