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Who is Mistaken?
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Recognizing when people have false beliefs is crucial for understanding their actions. We introduce the novel problem of identifying when people in abstract scenes have incorrect beliefs. We present a dataset of scenes, each visually depicting an 8-frame story in which a character has a mistaken belief. We then create a representation of characters' beliefs for two tasks in human action understanding: predicting who is mistaken, and when they are mistaken. Experiments suggest that our method for identifying mistaken characters performs better on these tasks than simple baselines. Diagnostics on our model suggest it learns important cues for recognizing mistaken beliefs, such as gaze. We believe models of people's beliefs will have many applications in action understanding, robotics, and healthcare.
Forward citations
Cited by 2 Pith papers
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From Black Boxes to Transparent Minds: Evaluating and Enhancing the Theory of Mind in Multimodal Large Language Models
Attention heads in multimodal LLMs linearly encode agents' beliefs, and steering those heads along probe-derived directions improves first- and second-order belief accuracy on the new GridToM benchmark.
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A Systematic Review on the Evaluation of Large Language Models in Theory of Mind Tasks
A review of 58 papers finds that large language models pass some Theory of Mind tests but remain brittle and fall short of human performance.
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