@inproceedings{liu-etal-2024-enhancing-legal,
title = "Enhancing Legal Expertise in Large Language Models through Composite Model Integration: The Development and Evaluation of Law-Neo",
author = "Liu, Zhihao and
Zhu, Yanzhen and
Lu, Mengyuan",
editor = "Aletras, Nikolaos and
Chalkidis, Ilias and
Barrett, Leslie and
Goan{\textcommabelow{t}}{\u{a}}, C{\u{a}}t{\u{a}}lina and
Preo{\textcommabelow{t}}iuc-Pietro, Daniel and
Spanakis, Gerasimos",
booktitle = "Proceedings of the Natural Legal Language Processing Workshop 2024",
month = nov,
year = "2024",
address = "Miami, FL, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.nllp-1.3",
pages = "33--41",
abstract = "Although large language models (LLMs) like ChatGPT have demonstrated considerable capabilities in general domains, they often lack proficiency in specialized fields. Enhancing a model{'}s performance in a specific domain, such as law, while maintaining low costs, has been a significant challenge. Existing methods, such as fine-tuning or building mixture of experts (MoE) models, often struggle to balance model parameters, training costs, and domain-specific performance. Inspired by composition to augment language models, we have developed Law-Neo, a novel model designed to enhance legal LLMs. This model significantly improves the model{'}s legal domain expertise at minimal training costs, while retaining the logical capabilities of a large-scale anchor model. Our Law-Neo model outperformed other models in comprehensive experiments on multiple legal task benchmarks, demonstrating the effectiveness of this approach.",
}
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<abstract>Although large language models (LLMs) like ChatGPT have demonstrated considerable capabilities in general domains, they often lack proficiency in specialized fields. Enhancing a model’s performance in a specific domain, such as law, while maintaining low costs, has been a significant challenge. Existing methods, such as fine-tuning or building mixture of experts (MoE) models, often struggle to balance model parameters, training costs, and domain-specific performance. Inspired by composition to augment language models, we have developed Law-Neo, a novel model designed to enhance legal LLMs. This model significantly improves the model’s legal domain expertise at minimal training costs, while retaining the logical capabilities of a large-scale anchor model. Our Law-Neo model outperformed other models in comprehensive experiments on multiple legal task benchmarks, demonstrating the effectiveness of this approach.</abstract>
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%0 Conference Proceedings
%T Enhancing Legal Expertise in Large Language Models through Composite Model Integration: The Development and Evaluation of Law-Neo
%A Liu, Zhihao
%A Zhu, Yanzhen
%A Lu, Mengyuan
%Y Aletras, Nikolaos
%Y Chalkidis, Ilias
%Y Barrett, Leslie
%Y Goan\textcommabelowtă, Cătălina
%Y Preo\textcommabelowtiuc-Pietro, Daniel
%Y Spanakis, Gerasimos
%S Proceedings of the Natural Legal Language Processing Workshop 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, FL, USA
%F liu-etal-2024-enhancing-legal
%X Although large language models (LLMs) like ChatGPT have demonstrated considerable capabilities in general domains, they often lack proficiency in specialized fields. Enhancing a model’s performance in a specific domain, such as law, while maintaining low costs, has been a significant challenge. Existing methods, such as fine-tuning or building mixture of experts (MoE) models, often struggle to balance model parameters, training costs, and domain-specific performance. Inspired by composition to augment language models, we have developed Law-Neo, a novel model designed to enhance legal LLMs. This model significantly improves the model’s legal domain expertise at minimal training costs, while retaining the logical capabilities of a large-scale anchor model. Our Law-Neo model outperformed other models in comprehensive experiments on multiple legal task benchmarks, demonstrating the effectiveness of this approach.
%U https://aclanthology.org/2024.nllp-1.3
%P 33-41
Markdown (Informal)
[Enhancing Legal Expertise in Large Language Models through Composite Model Integration: The Development and Evaluation of Law-Neo](https://aclanthology.org/2024.nllp-1.3) (Liu et al., NLLP 2024)
ACL