@inproceedings{singh-etal-2026-evaluating,
title = "Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification",
author = "Singh, Amrita and
Karaca, H. Suhan and
Joshi, Aditya and
Paik, Hye-young and
Jiang, Jiaojiao",
editor = "Mysore, Sheshera and
Kumar, Sachin and
Balachandran, Vidhisha and
Hayati, Shirley Anugrah and
Brahman, Faeze and
Moussa, Hanane Nour and
Salemi, Alireza",
booktitle = "Proceedings of the Second Workshop on Customizable {NLP}: Progress and Challenges in Customizing {NLP} for a Domain, Application, Group, or Individual ({C}ustom{NLP}4{U})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.customnlp4u-1.5/",
pages = "44--54",
ISBN = "979-8-89176-396-8",
abstract = "Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as `legal-specific' models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69{\%} fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications."
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<abstract>Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as ‘legal-specific’ models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69% fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications.</abstract>
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%0 Conference Proceedings
%T Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification
%A Singh, Amrita
%A Karaca, H. Suhan
%A Joshi, Aditya
%A Paik, Hye-young
%A Jiang, Jiaojiao
%Y Mysore, Sheshera
%Y Kumar, Sachin
%Y Balachandran, Vidhisha
%Y Hayati, Shirley Anugrah
%Y Brahman, Faeze
%Y Moussa, Hanane Nour
%Y Salemi, Alireza
%S Proceedings of the Second Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-396-8
%F singh-etal-2026-evaluating
%X Despite advances in legal NLP, no comprehensive evaluation of Transformer-based models customized for legal tasks (referred to as ‘legal-specific’ models in this paper) exists for contract classification tasks. To address this gap, we present an evaluation of 13 legal-specific transformer-based models on 3 English-language contract classification tasks and compare them with 9 generalist models. The results show that legal-specific models consistently outperform generalist models, especially on tasks requiring nuanced legal understanding. They also help reduce misclassification of rare classes in imbalanced datasets. Legal-BERT and Contracts-BERT establish new SOTAs on two of the three tasks, despite having 69% fewer parameters than the best-performing generalist models. We also identify CaseLaw-BERT and LexLM as strong additional baselines for contract classification. Our results highlight the shortcomings of generalist models, emphasizing the need for domain-specific customization, particularly in the context of legal applications.
%U https://aclanthology.org/2026.customnlp4u-1.5/
%P 44-54
Markdown (Informal)
[Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification](https://aclanthology.org/2026.customnlp4u-1.5/) (Singh et al., CustomNLP4U 2026)
ACL
- Amrita Singh, H. Suhan Karaca, Aditya Joshi, Hye-young Paik, and Jiaojiao Jiang. 2026. Evaluating Customized vs. Generalist Transformer-based Models for Legal Contract Classification. In Proceedings of the Second Workshop on Customizable NLP: Progress and Challenges in Customizing NLP for a Domain, Application, Group, or Individual (CustomNLP4U), pages 44–54, San Diego, California, USA. Association for Computational Linguistics.