@inproceedings{maslaris-arampatzis-2024-duth,
title = "{DUT}h at {S}em{E}val 2024 Task 5: A multi-task learning approach for the Legal Argument Reasoning Task in Civil Procedure",
author = "Maslaris, Ioannis and
Arampatzis, Avi",
editor = {Ojha, Atul Kr. and
Do{\u{g}}ru{\"o}z, A. Seza and
Tayyar Madabushi, Harish and
Da San Martino, Giovanni and
Rosenthal, Sara and
Ros{\'a}, Aiala},
booktitle = "Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)",
month = jun,
year = "2024",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.semeval-1.152/",
doi = "10.18653/v1/2024.semeval-1.152",
pages = "1053--1057",
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 toincorporate the rationales into the fine-tuning process."
}
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<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 toincorporate the rationales into the fine-tuning process.</abstract>
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%0 Conference Proceedings
%T DUTh at SemEval 2024 Task 5: A multi-task learning approach for the Legal Argument Reasoning Task in Civil Procedure
%A Maslaris, Ioannis
%A Arampatzis, Avi
%Y Ojha, Atul Kr.
%Y Doğruöz, A. Seza
%Y Tayyar Madabushi, Harish
%Y Da San Martino, Giovanni
%Y Rosenthal, Sara
%Y Rosá, Aiala
%S Proceedings of the 18th International Workshop on Semantic Evaluation (SemEval-2024)
%D 2024
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F maslaris-arampatzis-2024-duth
%X 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 toincorporate the rationales into the fine-tuning process.
%R 10.18653/v1/2024.semeval-1.152
%U https://aclanthology.org/2024.semeval-1.152/
%U https://doi.org/10.18653/v1/2024.semeval-1.152
%P 1053-1057
Markdown (Informal)
[DUTh at SemEval 2024 Task 5: A multi-task learning approach for the Legal Argument Reasoning Task in Civil Procedure](https://aclanthology.org/2024.semeval-1.152/) (Maslaris & Arampatzis, SemEval 2024)
ACL