Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866914061104447488 |
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| author | Imperial, Joseph Marvin Madabushi, Harish Tayyar |
| author_facet | Imperial, Joseph Marvin Madabushi, Harish Tayyar |
| contents | Policy compliance assessment is a fundamental task of evaluating whether an input case strictly complies with a set of human-defined rules, more generally known as policies. In practice, human experts follow a systematic, step-by-step process to identify violations with respect to specific stipulations outlined in the policy. However, such documentation of gold-standard, expert-level reasoning processes is costly to acquire. In this paper, we introduce Policy Reasoning Traces (PRT), a form of specialized generated reasoning chains that serve as a reasoning bridge to improve an LLM's policy compliance assessment capabilities. Our empirical evaluations demonstrate that the use of PRTs for both inference-time and training-time scenarios significantly enhances the performance of open-weight and commercial models, setting a new state-of-the-art for HIPAA and GDPR policies. Beyond accuracy gains, we also highlight how PRTs can improve an LLM's ability to accurately cite policy clauses, as well as influence compliance decisions through their high utilization from the raw chains of thought. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_23291 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces Imperial, Joseph Marvin Madabushi, Harish Tayyar Computation and Language Machine Learning Policy compliance assessment is a fundamental task of evaluating whether an input case strictly complies with a set of human-defined rules, more generally known as policies. In practice, human experts follow a systematic, step-by-step process to identify violations with respect to specific stipulations outlined in the policy. However, such documentation of gold-standard, expert-level reasoning processes is costly to acquire. In this paper, we introduce Policy Reasoning Traces (PRT), a form of specialized generated reasoning chains that serve as a reasoning bridge to improve an LLM's policy compliance assessment capabilities. Our empirical evaluations demonstrate that the use of PRTs for both inference-time and training-time scenarios significantly enhances the performance of open-weight and commercial models, setting a new state-of-the-art for HIPAA and GDPR policies. Beyond accuracy gains, we also highlight how PRTs can improve an LLM's ability to accurately cite policy clauses, as well as influence compliance decisions through their high utilization from the raw chains of thought. |
| title | Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2509.23291 |