SubmissionNumber#=%=#139
FinalPaperTitle#=%=#eagerlearners at SemEval2024 Task 5: The Legal Argument Reasoning Task in Civil Procedure
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Hoorieh Sabzevari
JobTitle#==#
Organization#==#Iran University of Science and Technology (IUST), University St., Hengam St., Resalat Square, Tehran, Iran
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.
Author{1}{Firstname}#=%=#Hoorieh
Author{1}{Lastname}#=%=#Sabzevari
Author{1}{Username}#=%=#lhoorie
Author{1}{Email}#=%=#hoorieh95@gmail.com
Author{1}{Affiliation}#=%=#Iran University of Science and Technology
Author{2}{Firstname}#=%=#Mohammadmostafa
Author{2}{Lastname}#=%=#Rostamkhani
Author{2}{Username}#=%=#mohammadmostafa
Author{2}{Email}#=%=#mohammadmostafarostamkhani@gmail.com
Author{2}{Affiliation}#=%=#IUST
Author{3}{Firstname}#=%=#Sauleh
Author{3}{Lastname}#=%=#Eetemadi
Author{3}{Username}#=%=#sauleh
Author{3}{Email}#=%=#sauleh@iust.ac.ir
Author{3}{Affiliation}#=%=#Iran University of Science and Technology

==========
èéáğö