Scaling Policy Compliance Assessment in Language Models with Policy Reasoning Traces

2025-09-27

AI Summary

This work addresses the high cost of expert-annotated reasoning chains for policy compliance assessment using large language models (LLMs). We propose Policy Reasoning Chains (PRT), a method that automatically generates structured, interpretable reasoning paths linking input cases to complex regulatory provisions (e.g., HIPAA, GDPR), eliminating the need for manually authored reference reasoning documents. PRT integrates generative reasoning chain construction, chain-of-thought distillation, and supervised fine-tuning, jointly optimized across both open-source and commercial LLMs. Experiments demonstrate that PRT achieves state-of-the-art performance in both policy violation detection accuracy and precise clause citation. It significantly enhances the reliability and interpretability of compliance judgments while enabling efficient, scalable policy auditing under low-resource conditions.

Technology Category

• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Natural Language Processing: Safety and Robustness
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

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.

Problem

• Automating policy compliance assessment using reasoning traces
• Reducing expert dependency for policy violation identification
• Improving LLM accuracy in legal clause citation

Innovation

• Policy Reasoning Traces bridge reasoning for compliance assessment
• PRTs enhance both inference-time and training-time model performance
• PRTs improve clause citation accuracy and influence compliance decisions