New Information-Theoretic Metric Measures Semantic Progress in Multi-turn Dialogue
Researchers have introduced a new metric for evaluating multi-turn dialogue quality by measuring how much new, relevant, and non-redundant information accumulates across conversation turns. The metric formalizes this 'semantic progress' as question-conditioned uncertainty reduction, approximated in embedding space using a Gaussian formulation with closed-form updates. It offers a reproducible, computationally lightweight alternative to LLM-as-a-judge evaluation methods, with competitive or improved alignment to human judgments on several benchmarks.
A preprint submitted to arXiv proposes a new framework for evaluating multi-turn dialogue systems by quantifying 'semantic progress'—the degree to which a conversation accumulates new, question-relevant, and non-redundant information over successive turns. The authors formalize this concept as question-conditioned uncertainty reduction and derive an information-theoretic metric that approximates it in embedding space using a tractable Gaussian formulation with closed-form updates. A complementary maximum-entropy argument explains why a log-determinant structure emerges naturally when only second-order embedding statistics are retained. The metric exhibits desirable theoretical properties such as monotonicity, additive decomposition of information gain across turns, and diminishing returns for redundant content. Evaluated on MT-Bench, Chatbot Arena, and UltraFeedback, the metric achieves competitive agreement with human judgments and outperforms several LLM-based judges on two of the three benchmarks. Crucially, the approach requires no autoregressive inference at evaluation time and runs effectively on lightweight embedding models under CPU-only conditions, making it accessible without large computational resources.
What's missing
As a preprint, this work has not yet undergone peer review. The study evaluates semantic progress as one dimension of dialogue quality but does not address other dimensions such as factual accuracy, coherence, or helpfulness, leaving open how well the metric generalizes to holistic dialogue evaluation.
What different sources said
- arXiv cs.LGCenter
Measuring Semantic Progress in Multi-turn Dialogue via Information Gain
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