Multi-Turn Dialogue Annotation
Labeling conversational quality across turns, including coherence and policy compliance over a whole conversation.
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.
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 in the exchange.
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 should not go. Annotators need to read the full transcript, not just the final exchange, to catch this class of failure.
Dialogue coherence and consistency scoring, sometimes called conversation coherence scoring, is usually one dimension nested inside this broader annotation task, alongside factual consistency across turns and whether the model tracks user preferences stated earlier in the conversation.
Related terms
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