Meta-Prompting Follow-Ups for Unsupervised Dialogue Evaluation Using Open-Source Large Language Models

Abstract

Automatically evaluating dialogue quality remains a major challenge due to the complexity and contextual variability of human interactions. This paper introduces DIET, a novel unsupervised, reference-free metric that uses follow-up utterances to assess dialogue quality. Unlike existing reference-free metrics, which rely on follow-ups derived from annotated data and apply a uniform set of utterances across all dialogues, DIET generates follow-ups using open-source Large Language Models (LLMs) and refines them through a selection process. Two strategies are explored: SELFMAP, where generation and evaluation are performed by the same model to ensure internal coherence, and CRAFT, where multiple models collaborate to generate diverse and complementary follow-ups, enhancing robustness and reducing model bias. Dialogue quality is measured via the likelihood of an LLM continuing the dialogue from selected follow-ups. Experiments show DIET better correlates with human judgments than existing reference-free metrics across multiple meta-evaluation datasets.

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