Evaluating large language models in theory of mind tasks
By Michal Kosinski. Proceedings of the National Academy of Sciences of the United States of America, 2023
378 citations recorded by the source corpus.
Significance Humans automatically and effortlessly track others’ unobservable mental states, such as their knowledge, intentions, beliefs, and desires. This ability—typically called “theory of mind” (ToM)—is fundamental to human social interactions, communication, empathy, consciousness, moral judgment, and religious beliefs. Our results show that recent large language models (LLMs) can solve false-belief tasks, typically used to evaluate ToM in humans. Regardless of how we interpret these outcomes, they signify the advent of more powerful and socially skilled AI—with profound positive and negative implications.
Subfields: Artificial Intelligence, Cultural Studies, Developmental and Educational Psychology
View Paper: https://www.semanticscholar.org/paper/3d48f1b4f69f7d22a47fa1599a1fc76277ba0c65
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Shizhu He
- Hayden S. Helm
- Carey E. Priebe
- Jingping Liu
- Yang Tang
- Tianyu Wu
- Kang Liu
- Brandon Duderstadt
In Cultural Studies
In Developmental and Educational Psychology
Citations in the corpus
- A Brief Overview of ChatGPT: The History, Status Quo and Potential Future Development (2023, background): “Please refer to [122] for a more comprehensive survey of the field.”
- Tracking the perspectives of interacting language models (2024, background): “In-sofar as an individual model is an intriguing proxy for an individual human 1 (Helm et al., 2023), a system of interacting language models is an in-triguing proxy for human communities.”