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Chapter 7 · 2024

Towards Automated Evaluation of LLM-based Multi-turn Dialogue Systems

Zekun Li, Wenhu Chen, Shiyang Li

Abstract

We propose an automated evaluation framework for multi-turn dialogue systems that assesses coherence, consistency, and task completion across extended conversations.

Eigenvector Insight — Zone III / PASF-PADE AnalysisNot part of the original paper
Eigenvector Research — Marco van Hurne
How this paper contributes to solving the Zone III problem (PASF-PADE)

Automated evaluation of multi-turn interactions is a Zone III testing requirement. You cannot manually review every step of a long-running enterprise workflow. Automated evaluation that checks coherence, consistency, and task completion across extended interactions is the foundation of Zone III quality assurance. This paper provides the methodology.

Why AI is not sufficient for Zone III without this

Zone III refers to high-complexity, high-risk, long-running agentic workflows — the class of enterprise AI deployments where a single failure can cascade across hundreds of steps. Standard AI models, trained to predict the next token, are not inherently designed for durable, governed, multi-step execution. This paper addresses one or more of the structural gaps that make Zone III deployments unsafe without explicit architectural intervention.

Topics

dialogue evaluationmulti-turncoherenceautomated testing