{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T20:10:10Z","timestamp":1751919010587,"version":"3.41.2"},"reference-count":74,"publisher":"MIT Press","license":[{"start":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T00:00:00Z","timestamp":1751846400000},"content-version":"vor","delay-in-days":187,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["direct.mit.edu"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,7,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>How do people understand and evaluate claims about others\u2019 beliefs, even though these beliefs cannot be directly observed? In this paper, we introduce a cognitive model of epistemic language interpretation, grounded in Bayesian inferences about other agents\u2019 goals, beliefs, and intentions: a language-augmented Bayesian theory-of-mind (LaBToM). By translating natural language into an epistemic \u201clanguage-of-thought\u201d with grammar-constrained LLM decoding, then evaluating these translations against the inferences produced by inverting a generative model of rational action and perception, LaBToM captures graded plausibility judgments of epistemic claims. We validate our model in an experiment where participants watch an agent navigate a maze to find keys hidden in boxes needed to reach their goal, then rate sentences about the agent\u2019s beliefs. In contrast with multimodal LLMs (GPT-4o, Gemini Pro) and ablated models, our model correlates highly with human judgments for a wide range of expressions, including modal language, uncertainty expressions, knowledge claims, likelihood comparisons, and attributions of false belief.<\/jats:p>","DOI":"10.1162\/tacl_a_00752","type":"journal-article","created":{"date-parts":[[2025,7,7]],"date-time":"2025-07-07T19:37:01Z","timestamp":1751917021000},"page":"613-637","update-policy":"https:\/\/doi.org\/10.1162\/mitpressjournals.corrections.policy","source":"Crossref","is-referenced-by-count":0,"title":["Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind"],"prefix":"10.1162","volume":"13","author":[{"given":"Lance","family":"Ying","sequence":"first","affiliation":[{"name":"Massachusetts Institute of Technology, Cambridge, MA, USA"},{"name":"Harvard University, Cambridge, MA, 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