1. Do Large Language Models Know What Humans Know?
- Author
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Trott, Sean, Jones, Cameron, Chang, Tyler, Michaelov, James, and Bergen, Benjamin
- Subjects
Cognitive and Computational Psychology ,Psychology ,Behavioral and Social Science ,Clinical Research ,Basic Behavioral and Social Science ,Underpinning research ,1.2 Psychological and socioeconomic processes ,1.1 Normal biological development and functioning ,Mental health ,Good Health and Well Being ,Child ,Humans ,Communication ,Deception ,Language ,Child Development ,Theory of Mind ,Large language models ,False Belief Task ,Belief attribution ,Artificial Intelligence and Image Processing ,Cognitive Sciences ,Experimental Psychology ,Applied and developmental psychology ,Biological psychology ,Cognitive and computational psychology - Abstract
Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test 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. In pre-registered analyses, we present a linguistic version of the False Belief Task to both human participants and a large language model, GPT-3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans nor does it explain the full extent of their behavior-despite being exposed to more language than a human would in a lifetime. This 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.
- Published
- 2023