Beyond Single-Policy: Evaluating Composed Organization-Specific Policy Alignment in LLM Chatbots

2026-02-17 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning · Depth: Expert, medium

Summary

COPAL is an automated framework designed to evaluate composed-policy alignment in large language model (LLM) chatbots, addressing a critical gap in existing benchmarks. Traditional evaluations test policies individually, overlooking complex scenarios where a single user request involves multiple organizational policies. An audit of deployed chatbots revealed 47.6% of real-world cases involve multiple policies, being three times more error-prone. COPAL generates queries based on empirically derived interaction patterns, each paired with an explicit handling contract specifying required and prohibited content. Applied across 30 organization-like company worlds and 9 served models, COPAL identified a significant 33.1% error rate for composed-policy requests, highlighting a persistent challenge for current LLMs.

Key takeaway

For MLOps Engineers deploying LLM chatbots in regulated environments, you must move beyond single-policy evaluations. Your current benchmarks likely overestimate policy alignment, leaving critical multi-policy violations undetected until deployment. Implement frameworks like COPAL to test composed-policy scenarios, explicitly defining what responses should provide and avoid. This proactive approach will significantly reduce compliance risks and enhance chatbot reliability in complex organizational settings.

Key insights

LLM chatbots struggle with requests requiring simultaneous adherence to multiple organizational policies, a gap COPAL evaluates.

Principles

• Single-policy tests overestimate LLM alignment.
• Policy rules require explicit trigger, scope, and effect grounding.
• Composed-policy failures often satisfy only one constraint.

Method

COPAL grounds policies into clauses, constructs compositions via four interaction patterns, generates queries with handling contracts, and evaluates responses against these contracts.

In practice

• Audit real-traffic data for multi-policy interactions.
• Define explicit handling contracts for complex queries.

Topics

• LLM Chatbots
• Policy Alignment
• Composed Policies
• Evaluation Frameworks
• Organizational Policies
• Compliance Testing

Best for: AI Architect, Research Scientist, CTO, AI Scientist, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.