eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure
Hoorieh Sabzevari, Mohammadmostafa Rostamkhani, Sauleh Eetemadi
Abstract
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.- Anthology ID:
- 2024.semeval-1.133
- Volume:
- Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Atul Kr. Ojha, A. Seza Doğruöz, Harish Tayyar Madabushi, Giovanni Da San Martino, Sara Rosenthal, Aiala Rosá
- Venue:
- SemEval
- SIG:
- SIGLEX
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 922–926
- Language:
- URL:
- https://aclanthology.org/2024.semeval-1.133
- DOI:
- Cite (ACL):
- Hoorieh Sabzevari, Mohammadmostafa Rostamkhani, and Sauleh Eetemadi. 2024. eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure. In Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024), pages 922–926, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure (Sabzevari et al., SemEval 2024)
- PDF:
- https://preview.aclanthology.org/jeptaln-2024-ingestion/2024.semeval-1.133.pdf