eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure
This work addresses legal reasoning for law students and practitioners, but it is incremental as it applies existing methods to a specific domain.
The study tackled the problem of classifying legal argument reasoning data in civil procedure using zero-shot large language models, achieving a highest F1 score of 64%.
This study investigates the performance of the zero-shot method in classifying data using three large language models, alongside two models with large input token sizes and the two pre-trained models on legal data. Our main dataset comes from the domain of U.S. civil procedure. It includes summaries of legal cases, specific questions, potential answers, and detailed explanations for why each solution is relevant, all sourced from a book aimed at law students. By comparing different methods, we aimed to understand how effectively they handle the complexities found in legal datasets. Our findings show how well the zero-shot method of large language models can understand complicated data. We achieved our highest F1 score of 64% in these experiments.