Do Large Language Models know what humans know?
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Summary
Testing the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible.
- Type
- preprint
- Published
- 2022-09-04
- Cited by
- 145
- References
- 80
- Access
- Open access
- OpenAlex
- https://openalex.org/W4297631225
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:252089182
Keywords
Psychology, Cognitive psychology, Task (project management), Test (biology), Human language
References
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Cited by
- Can Language Models Trained on Written Monologue Learn to Predict Spoken Dialogue?
- Event knowledge in large language models: the gap between the impossible and the unlikely
- Using cognitive psychology to understand GPT-3
- Evaluating large language models in theory of mind tasks
- The debate over understanding in AI’s large language models
- Machine Psychology: Investigating Emergent Capabilities and Behavior in Large Language Models Using Psychological Methods
- Boosting Theory-of-Mind Performance in Large Language Models via Prompting
- Understanding Social Reasoning in Language Models with Language Models
- The SocialAI School: Insights from Developmental Psychology Towards Artificial Socio-Cultural Agents
- Situated Natural Language Explanations
- Unveiling Theory of Mind in Large Language Models: A Parallel to Single Neurons in the Human Brain
- A Bayesian theory of mind approach to modeling cooperation and communication
- Evaluating large language models' ability to understand metaphor and sarcasm using a screening test for Asperger syndrome
- How FaR Are Large Language Models From Agents with Theory-of-Mind?
- Using large language models in psychology
- Co-constructing knowledge with generative AI tools: Reflections from a CSCL perspective
- Towards A Holistic Landscape of Situated Theory of Mind in Large Language Models
- Large Language Models: The Need for Nuance in Current Debates and a Pragmatic Perspective on Understanding
- Individuality and the collective in AI agents: Explorations of shared consciousness and digital homunculi in the metaverse for cultural heritage
- Language Model Behavior: A Comprehensive Survey
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