SubmissionNumber#=%=#61
FinalPaperTitle#=%=#0x.Yuan at SemEval-2024 Task 5: Enhancing Legal Argument Reasoning with Structured Prompts
ShortPaperTitle#=%=#
NumberOfPages#=%=#6
CopyrightSigned#=%=#Yu-An Lu
JobTitle#==#
Organization#==#National Chupei High School No.3,Jhongyang RD. Jhubei City, Hsinchu County 30252, Taiwan, R.O.C
30252
Abstract#==#The intersection of legal reasoning and Natural Language Processing (NLP) technologies, particularly Large Language Models (LLMs), offers groundbreaking potential for augmenting human capabilities in the legal domain. This paper presents our approach and findings from participating in SemEval-2024 Task 5, focusing on the effect of argument reasoning in civil procedures using legal reasoning prompts. We investigated the impact of structured legal reasoning methodologies, including TREACC, IRAC, IRAAC, and MIRAC, on guiding LLMs to analyze and evaluate legal arguments systematically. Our experimental setup involved crafting specific prompts based on these methodologies to instruct the LLM to dissect and scrutinize legal cases, aiming to discern the cogency of argumentative solutions within a zero-shot learning framework. The performance of our approach, as measured by F1 score and accuracy, demonstrated the efficacy of integrating structured legal reasoning into LLMs for legal analysis. The findings underscore the promise of LLMs, when equipped with legal reasoning prompts, in enhancing their ability to process and reason through complex legal texts, thus contributing to the broader application of AI in legal studies and practice.
Author{1}{Firstname}#=%=#Yu-An
Author{1}{Lastname}#=%=#Lu
Author{1}{Username}#=%=#yuanlu0
Author{1}{Email}#=%=#luyuam0@gmail.com
Author{1}{Affiliation}#=%=#National Chupei High School
Author{2}{Firstname}#=%=#Hung-Yu
Author{2}{Lastname}#=%=#Kao
Author{2}{Email}#=%=#hykao@mail.ncku.edu.tw
Author{2}{Affiliation}#=%=#National Cheng Kung University

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