Introduces NCP-ExploreToM framework to evaluate LLMs on inducing belief states via planning and action, with GPT-5 succeeding on ~80% of tasks and outperforming humans.
arXiv preprint arXiv:2504.10839 , year=
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An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
GroupToM-Bench is presented as the first multimodal benchmark for group-level Theory of Mind spanning micro BDI states to macro outcome prediction, with experiments showing current MLLMs lag human baselines on nonlinear social dynamics.
LLM outputs are meaningful according to standard theories of human language, without requiring anthropomorphic assumptions about the models.
LLMs excel at retrospective mental-state labeling on naturalistic dialogues but mostly fail a context-free prospective probe that maps isolated mental-state profiles to dialogue trajectories, despite expert 100% accuracy.
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.
citing papers explorer
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Theory of Mind and Persuasion Beyond Conversation: Assessing the Capacity of LLMs to Induce Belief States via Planning and Action
Introduces NCP-ExploreToM framework to evaluate LLMs on inducing belief states via planning and action, with GPT-5 succeeding on ~80% of tasks and outperforming humans.
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Embodied Explainability and Ontological Obstacles: Why We Struggle to Explain the Answers of Large Language Models (LLMs)
An argument paper reframes LLM explainability as an embodied, situated practice based on Dourish and enactivist cognition, identifying ontological obstacles in internal explanations and advocating affordance-based designs.
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GroupToM-Bench: Benchmarking Group Theory of Mind and Nonlinear Social Emergence in MLLMs
GroupToM-Bench is presented as the first multimodal benchmark for group-level Theory of Mind spanning micro BDI states to macro outcome prediction, with experiments showing current MLLMs lag human baselines on nonlinear social dynamics.
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Chatbots Output Meaningful (but Problematic) Language
LLM outputs are meaningful according to standard theories of human language, without requiring anthropomorphic assumptions about the models.
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DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories
LLMs excel at retrospective mental-state labeling on naturalistic dialogues but mostly fail a context-free prospective probe that maps isolated mental-state profiles to dialogue trajectories, despite expert 100% accuracy.
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Medical Model Synthesis Architectures: A Case Study
MedMSA framework retrieves knowledge via language models then builds formal probabilistic models to produce uncertainty-weighted differential diagnoses from symptoms.