evaluation

Multi-Turn Dialogue Annotation

Labeling conversational quality across turns, including coherence and policy compliance over a whole conversation.

Multi-turn dialogue annotation evaluates a conversation as a whole rather than judging each response in isolation — checking whether the model remembers earlier context, stays consistent across turns, and handles a conversation that gradually shifts (sometimes deliberately, as in a slow-building jailbreak attempt) without losing track of policy constraints established earlier.

This is meaningfully harder than single-turn evaluation because failures can be cumulative: no single response looks wrong on its own, but the conversation as a whole drifts somewhere it shouldn't. Annotators need to read the full transcript, not just the final exchange, to catch this class of failure.

Dialogue coherence and consistency scoring (see conversation coherence scoring) is usually one dimension nested inside this broader annotation task.

What this means for trainers

Always read the full conversation history before judging the latest turn — evaluating the last message in isolation is the single most common mistake in this task type.