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    <titleInfo>
        <title>Policy Compliance of User Requests in Natural Language for AI Systems</title>
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            <namePart type="given">Yunyao</namePart>
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            <namePart type="given">Georg</namePart>
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    <abstract>Consider an organization whose users send requests in natural language to an AI system that fulfills them by carrying out specific tasks. In this paper, we consider the problem of ensuring such user requests comply with a list of diverse policies determined by the organization with the purpose of guaranteeing the safe and reliable use of the AI system. We propose, to the best of our knowledge, the first benchmark consisting of annotated user requests of diverse compliance with respect to a list of policies. Our benchmark is related to industrial applications in the technology sector. We then use our benchmark to evaluate the performance of various LLM models on policy compliance assessment under different solution methods. We analyze the differences on performance metrics across the models and solution methods, showcasing the challenging nature of our problem.</abstract>
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        <url>https://aclanthology.org/2026.acl-industry.21/</url>
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%0 Conference Proceedings
%T Policy Compliance of User Requests in Natural Language for AI Systems
%A Cisneros-Velarde, Pedro
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F cisneros-velarde-2026-policy
%X Consider an organization whose users send requests in natural language to an AI system that fulfills them by carrying out specific tasks. In this paper, we consider the problem of ensuring such user requests comply with a list of diverse policies determined by the organization with the purpose of guaranteeing the safe and reliable use of the AI system. We propose, to the best of our knowledge, the first benchmark consisting of annotated user requests of diverse compliance with respect to a list of policies. Our benchmark is related to industrial applications in the technology sector. We then use our benchmark to evaluate the performance of various LLM models on policy compliance assessment under different solution methods. We analyze the differences on performance metrics across the models and solution methods, showcasing the challenging nature of our problem.
%U https://aclanthology.org/2026.acl-industry.21/
%P 318-328

Markdown (Informal)

[Policy Compliance of User Requests in Natural Language for AI Systems](https://aclanthology.org/2026.acl-industry.21/) (Cisneros-Velarde, ACL 2026)

• Policy Compliance of User Requests in Natural Language for AI Systems (Cisneros-Velarde, ACL 2026)

ACL

• Pedro Cisneros-Velarde. 2026. Policy Compliance of User Requests in Natural Language for AI Systems. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 318–328, San Diego, California, USA. Association for Computational Linguistics.