Evaluating Multi-Turn Conversations

A single-turn test checks one prompt and one response. In a multi-turn interaction, a model may need to remember constraints, use earlier facts, ask a clarifying question, recover from an error, or complete a goal over several steps. Evaluation should therefore score the whole trajectory as well as important individual turns. Useful dimensions include task success, context retention, consistency with earlier statements, adherence to constraints, appropriate clarification, recovery after correction, tool use, safety, latency, and user effort. The right dimensions depend on the application. A customer support task may require correct resolution and policy compliance; a writing assistant may need to preserve evolving preferences. Research benchmarks illustrate the scope of this work. MT-Bench-101 includes 1,388 multi-turn dialogues across 13 tasks and analyzes 4,208 turns. Other benchmarks test realistic instruction following over multiple turns. These samples can reveal failure modes, but they are not the same as deployment logs or a representative evaluation of a specific product. Automated judges can reduce scoring effort, but judge agreement, rubric quality, model bias, and prompt sensitivity require validation. Pair automated scores with human review for important cases. Test dialogue variations, user corrections, long contexts, and adversarial turns. Report sample selection, scoring rules, and failure categories. A strong single response does not prove that the assistant completed the user’s goal across the conversation. Tests should state the intended outcome in advance.