SubmissionNumber#=%=#160
FinalPaperTitle#=%=#DUTh at SemEval 2024 Task 5: A multi-task learning approach for the Legal Argument Reasoning Task in Civil Procedure
ShortPaperTitle#=%=#
NumberOfPages#=%=#5
CopyrightSigned#=%=#Ioannis Maslaris
JobTitle#==#
Organization#==#Database & Information Retrieval research unit,
Department of Electrical & Computer Engineering,
Democritus University of Thrace, Greece.
Abstract#==#Text-generative models have proven to be good reasoners. Although reasoning abilities are mostly observed in larger language models, a number of strategies try to transfer this skill to smaller language models. This paper presents our approach to SemEval 2024 Task-5: The Legal Argument Reasoning Task in Civil Procedure. This shared task aims to develop a system that efficiently handles a multiple-choice question-answering task in the context of the US civil procedure domain. The dataset provides a human-generated rationale for each answer. Given the complexity of legal issues, this task certainly challenges the reasoning abilities of LLMs and AI systems in general. Our work explores fine-tuning an LLM as a correct/incorrect answer classifier. In this context, we are making use of multi-task learning to
incorporate the rationales into the fine-tuning process.
Author{1}{Firstname}#=%=#Ioannis
Author{1}{Lastname}#=%=#Maslaris
Author{1}{Username}#=%=#yms1
Author{1}{Email}#=%=#yannismslr@gmail.com
Author{1}{Affiliation}#=%=#Democritus University of Thrace
Author{2}{Firstname}#=%=#Avi
Author{2}{Lastname}#=%=#Arampatzis
Author{2}{Username}#=%=#avi1
Author{2}{Email}#=%=#yannis.maslatis@gmail.com
Author{2}{Affiliation}#=%=#Democritus University of Thrace

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