Characterize semantic uncertainty in incremental utterance processing

Determine whether semantic uncertainty measures can characterize how an utterance becomes more or less semantically constrained as it unfolds, extending their use beyond question answering, summarization, and translation.

Background

The paper reviews semantic uncertainty methods that quantify variation in the meanings of language-model outputs, including cluster-based and embedding-based approaches. These methods have primarily been studied in conventional language-generation tasks rather than in incremental conversational processing.

The unresolved issue is whether such measures can track the evolving semantic constraints of a partial utterance as each new word arrives. Resolving this question would establish whether semantic uncertainty is suitable for modeling the expectations that listeners form during turn-taking.

References

Future work should therefore test whether the signal remains useful when integrated with models that incorporate these additional sources of information.

— Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking  (2609.10934 - Umair et al., 10 Sep 2026) in Section 7, Limitations

Less is known about whether these measures can characterize how an utterance becomes more or less semantically constrained as it unfolds.

— Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking  (2609.10934 - Umair et al., 10 Sep 2026) in Section 2.3, Semantic Uncertainty Quantification

Future work is required to determine whether semantic uncertainty can be deployed in a real-time system.

— Using Semantic Uncertainty to Estimate Transition Relevance in Turn-taking  (2609.10934 - Umair et al., 10 Sep 2026) in Section 7, Limitations