How do people understand and evaluate claims about others' 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' goals, beliefs, and intentions: a language-augmented Bayesian theory-of-mind (LaBToM). By translating natural language into an epistemic language-of-thought, then evaluating these translations against the inferences produced by inverting a probabilistic generative model of rational action and perception, LaBToM captures quantitative human judgments about initial and present belief claims. We validate our model in an experiment where participants evaluate belief sentences about an agent who has to find keys hidden in boxes to reach its goal. In contrast with multimodal LLMs (GPT-4o, Gemini Pro) and various ablations, our model produces a much stronger fit with human ratings for a wide range of epistemic expressions, including modal language, uncertainty expressions, knowledge claims, likelihood comparisons, and descriptions of false beliefs.

Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind

TACL/CL.1: TACL/CL Journal

technical paper

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Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind

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