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        <title>Contemporary LLMs struggle with extracting formal legal arguments</title>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2025</title>
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            <namePart type="given">Nikolaos</namePart>
            <namePart type="family">Aletras</namePart>
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            <namePart type="given">Leslie</namePart>
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    <abstract>Legal Argument Mining (LAM) is a complex challenge for humans and language models alike. This paper explores the application of Large Language Models (LLMs) in LAM, focusing on the identification of fine-grained argument types within judgment texts. We compare the performance of Flan-T5 and Llama 3 models against a baseline RoBERTa model to study if the advantages of magnitude-bigger LLMs can be leveraged for this task. Our study investigates the effectiveness of fine-tuning and prompting strategies in enhancing the models’ ability to discern nuanced argument types. Despite employing state-of-the-art techniques, our findings indicate that neither fine-tuning nor prompting could surpass the performance of a domain-pre-trained encoder-only model. This highlights the challenges and limitations in adapting general-purpose large language models to the specialized domain of legal argumentation. The insights gained from this research contribute to the ongoing discourse on optimizing NLP models for complex, domain-specific tasks. Our code and data for reproducibility are available at https://github.com/trusthlt/legal-argument-spans.</abstract>
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%0 Conference Proceedings
%T Contemporary LLMs struggle with extracting formal legal arguments
%A Held, Lena
%A Habernal, Ivan
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goanță, Cătălina
%Y Preoțiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-338-8
%F held-habernal-2025-contemporary
%X Legal Argument Mining (LAM) is a complex challenge for humans and language models alike. This paper explores the application of Large Language Models (LLMs) in LAM, focusing on the identification of fine-grained argument types within judgment texts. We compare the performance of Flan-T5 and Llama 3 models against a baseline RoBERTa model to study if the advantages of magnitude-bigger LLMs can be leveraged for this task. Our study investigates the effectiveness of fine-tuning and prompting strategies in enhancing the models’ ability to discern nuanced argument types. Despite employing state-of-the-art techniques, our findings indicate that neither fine-tuning nor prompting could surpass the performance of a domain-pre-trained encoder-only model. This highlights the challenges and limitations in adapting general-purpose large language models to the specialized domain of legal argumentation. The insights gained from this research contribute to the ongoing discourse on optimizing NLP models for complex, domain-specific tasks. Our code and data for reproducibility are available at https://github.com/trusthlt/legal-argument-spans.
%U https://aclanthology.org/2025.nllp-1.20/
%P 292-303

Markdown (Informal)

[Contemporary LLMs struggle with extracting formal legal arguments](https://aclanthology.org/2025.nllp-1.20/) (Held & Habernal, NLLP 2025)

• Contemporary LLMs struggle with extracting formal legal arguments (Held & Habernal, NLLP 2025)

ACL

• Lena Held and Ivan Habernal. 2025. Contemporary LLMs struggle with extracting formal legal arguments. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 292–303, Suzhou, China. Association for Computational Linguistics.