Understanding Epistemic Language with a Bayesian Theory of Mind
<|reference_start|>Understanding Epistemic Language with a Bayesian Theory of Mind: 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 graded plausibility judgments about 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's 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.<|reference_end|>
@article{ying2024understanding,
    title={Understanding Epistemic Language with a Bayesian Theory of Mind},
    author={Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B.
  Tenenbaum},
    journal={arXiv preprint arXiv:2408.12022},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.12022},
    primaryClass={cs.CL cs.AI}
}