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        <title>ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts</title>
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            <title>Proceedings of the Natural Legal Language Processing Workshop 2025</title>
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    <abstract>The potential of large language models (LLMs) in contract legal risk analysis remains underexplored. In response, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD), we assess 4 proprietary and 15 open-source LLMs. Our results highlight five key findings: (1) Proprietary models outperform open-source models in both correctness and output effectiveness. (2) Larger open-source models generally perform better, though the improvement slows down as models get bigger. (3) Reasoning (“thinking”) mode improves output effectiveness but reduces correctness, likely due to over-complicating simpler tasks. (4) Open-source models generate “no related clause” responses more frequently even when relevant clauses are present. (5) Model quantization speed up inference but at the cost of performance drop, showing the tradeoff between efficiency and accuracy. These findings suggest that while most LLMs perform at a level comparable to junior legal assistants, open-source models require targeted fine-tuning to ensure correctness and effectiveness in high-stakes legal settings. ContractEval offers a solid benchmark to guide future development of legal-domain LLMs.</abstract>
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%0 Conference Proceedings
%T ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts
%A Liu, Shuang
%A Li, Zelong
%A Ma, Ruoyun
%A Zhao, Haiyan
%A Du, Mengnan
%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 liu-etal-2025-contracteval
%X The potential of large language models (LLMs) in contract legal risk analysis remains underexplored. In response, this paper introduces ContractEval, the first benchmark to thoroughly evaluate whether open-source LLMs could match proprietary LLMs in identifying clause-level legal risks in commercial contracts. Using the Contract Understanding Atticus Dataset (CUAD), we assess 4 proprietary and 15 open-source LLMs. Our results highlight five key findings: (1) Proprietary models outperform open-source models in both correctness and output effectiveness. (2) Larger open-source models generally perform better, though the improvement slows down as models get bigger. (3) Reasoning (“thinking”) mode improves output effectiveness but reduces correctness, likely due to over-complicating simpler tasks. (4) Open-source models generate “no related clause” responses more frequently even when relevant clauses are present. (5) Model quantization speed up inference but at the cost of performance drop, showing the tradeoff between efficiency and accuracy. These findings suggest that while most LLMs perform at a level comparable to junior legal assistants, open-source models require targeted fine-tuning to ensure correctness and effectiveness in high-stakes legal settings. ContractEval offers a solid benchmark to guide future development of legal-domain LLMs.
%U https://aclanthology.org/2025.nllp-1.19/
%P 291-291

Markdown (Informal)

[ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts](https://aclanthology.org/2025.nllp-1.19/) (Liu et al., NLLP 2025)

• ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts (Liu et al., NLLP 2025)

ACL

• Shuang Liu, Zelong Li, Ruoyun Ma, Haiyan Zhao, and Mengnan Du. 2025. ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts. In Proceedings of the Natural Legal Language Processing Workshop 2025, pages 291–291, Suzhou, China. Association for Computational Linguistics.