Multi-Turn Dialogue Evaluation

This field studies how well AI systems handle changing goals, accumulated context, memory, tools, and user behavior over a conversation, where one mistake can shape later turns. Researchers build interactive benchmarks and user simulations for settings such as retrieval, medical consultation, task completion, emotional support, and spoken or multimodal dialogue, often comparing results with matched single-turn tests. Evaluation methods include human ratings and LLM judges, alongside turn- and session-level rubrics, execution checks, state tracking, and measures of error propagation and interaction cost. A central challenge is making evaluations realistic and diagnostically useful while ensuring that scores predict grounded outcomes: fluent or satisfying dialogue can still be wrong, unsafe, or ineffective.