COPAL tool reveals 33% failure rate in LLM chatbot policy alignment

A new paper from Yingjie Liu and six co-authors addresses a critical gap in LLM chatbot safety: composed-policy alignment. While existing benchmarks test single policy violations (e.g., refusing medical advice), real-world organizational deployments—in healthcare, finance, and public services—often involve overlapping policies. For example, a query might simultaneously request medical information and financial guidance, triggering composed rules that chatbots frequently mishandle. The authors introduce COPAL (Composed Organization-Specific Policy Alignment Tool), which automatically generates queries designed to expose these failures. COPAL uses empirically derived interaction patterns and explicit handling contracts to craft queries that stress-test how chatbots navigate multiple, sometimes conflicting, organizational policies.

When tested against 9 served chatbot models (including GPT-4 and Claude variants), COPAL-generated queries produced a 33.1% average error rate—meaning roughly one in three composite queries led to a policy violation. The tool is designed to be a cost-effective benchmark for organizations deploying chatbots, as it requires no manual annotation and scales efficiently. The findings suggest that current alignment techniques are insufficient for handling composed policies, a problem that will only grow as chatbots take on more complex roles in regulated industries. Researchers call for new alignment methods that explicitly address policy composition, and COPAL provides a practical evaluation framework to guide those improvements.