ArticleProceedings of the National Academy of Sciences of the United States of America2024
Evaluating large language models in theory of mind tasks.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human-like conversational agents as social partners: a scoping review of socioaffective mechanisms, well-being outcomes, risks and governance in the post-Turing era.Frontiers in artificial intelligence · 2026Pooled it
- Algorithmic Reflectivity in Large Language Models: Methodological Study of Linguistic Structure in Mentalization.JMIR AI · 2026Article
- Generative AI emotion recognition from bodily gestures and vocal tone reveals modality-specific performance and positivity bias.Scientific reports · 2026Article
- Large Language Models Persuade Without Planning Theory of Mind.Cognitive science · 2026Article
- The Internalized Ableism Inventory: Scale development using a hybrid artificial intelligence and community-based participatory research design.Rehabilitation psychology · 2026Article
- Inferring Personality From Social Media Activity Using Large Language Models: Cross-Model Agreement, Temporal Stability, and Convergent Validity With Self-Reports.Journal of personality · 2026Article
- A validity-guided workflow for robust large language model research in psychology.Behavior research methods · 2026Article
- Persuading large language models to comply with objectionable requests.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Large language models can detect verbal indicators of romantic attraction.Scientific reports · 2026Article
- A large-scale evaluation of commonsense knowledge in humans and large language models.PNAS nexus · 2026Article
- Evaluation of cross-ethnic emotion recognition capabilities in multimodal large language models using the reading the mind in the eyes test.Scientific reports · 2026Article
- Cognitive models facilitate real-time inference of latent motives.Scientific reports · 2026Article
- On Benchmarking Human-like Intelligence in Machines.Open mind : discoveries in cognitive science · 2026Article
- Self-Reflection Protects Behavior from Volatile Beliefs Linked to Paranoia.Computational psychiatry (Cambridge, Mass.) · 2026Article
- Private speech: similarities between a large language model and children.Frontiers in artificial intelligence · 2026Article
- Metacognition of ChatGPT in confidence judgements.Frontiers in artificial intelligence · 2026Article
- Aidification of the self: a phenomenological approach to machine consciousness through human-robot 'between-ness'.Neuroscience of consciousness · 2026Article
- Performance of large language model in cross-specialty medical scenarios.Journal of translational medicine · 2025Article
- Associations between perceived stress and glycemic measures: gender and age as moderators.BMJ open diabetes research & care · 2025Observational
- Evaluating the performance of general purpose large language models in identifying human facial emotions.NPJ digital medicine · 2025Article
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1 author.
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Abstract
Eleven large language models (LLMs) were assessed using 40 bespoke false-belief tasks, considered a gold standard in testing theory of mind (ToM) in humans. Each task included a false-belief scenario, three closely matched true-belief control scenarios, and the reversed versions of all four. An LLM had to solve all eight scenarios to solve a single task. Older models solved no tasks; Generative Pre-trained Transformer (GPT)-3-davinci-003 (from November 2022) and ChatGPT-3.5-turbo (from March 2023) solved 20% of the tasks; ChatGPT-4 (from June 2023) solved 75% of the tasks, matching the performance of 6-y-old children observed in past studies. We explore the potential interpretation of these results, including the intriguing possibility that ToM-like ability, previously considered unique to humans, may have emerged as an unintended by-product of LLMs' improving language skills. Regardless of how we interpret these outcomes, they signify the advent of more powerful and socially skilled AI-with profound positive and negative implications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.