Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation
Human language relies on unspoken beliefs and belief updates. This study creates the first expert-annotated implicature cancellation dataset and evaluates LLMs' ability to recognize unspoken beliefs through implicatures and understand their updates. Results show LLMs lag behind humans, especially in natural scenarios, and their performance depends on prior beliefs and update type.
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[Submitted on 27 Jul 2026]
Title:Evaluating Communicative Belief Updates in Large Language Models via Implicature Recognition and Cancellation
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Abstract:Human language is driven by unspoken beliefs and belief updates, making these critical to model for successful communication between large language models (LLMs) and their users. In this paper, we evaluate the ability of LLMs to recognize unspoken beliefs made through implicatures and to understand their updates through implicature cancellation: the pragmatic phenomenon whereby an utterance's implied meaning is weakened or negated. We create the first expert-annotated implicature cancellation dataset, [DatasetName], crowdsourced for human judgements of implicatures and their corresponding cancellations. We find that LLM belief update understanding lags behind that of humans, especially in more naturally-occurring scenarios. Additional control experiments suggest that successes in LLM belief updates may stem in part from a reliance on prior beliefs, and that failures in belief updates may depend on their type and on their form. Overall, our study suggests that current LLMs have not yet reached human-level understanding of unspoken beliefs and belief updates. Code and data are available at this https URL.
Subjects:
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
Cite as: arXiv:2607.25094 [cs.CL]
(or arXiv:2607.25094v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.25094
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Cesare Spinoso-Di Piano [view email] [v1] Mon, 27 Jul 2026 21:35:22 UTC (982 KB)
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