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. 2023 Oct;3(10):833-838.
doi: 10.1038/s43588-023-00527-x. Epub 2023 Oct 5.

Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT

Thilo Hagendorff  1 , Sarah Fabi  2 , Michal Kosinski  3
Affiliations

Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT

Thilo Hagendorff et al. Nat Comput Sci. 2023 Oct.

Abstract

We design a battery of semantic illusions and cognitive reflection tests, aimed to elicit intuitive yet erroneous responses. We administer these tasks, traditionally used to study reasoning and decision-making in humans, to OpenAI's generative pre-trained transformer model family. The results show that as the models expand in size and linguistic proficiency they increasingly display human-like intuitive system 1 thinking and associated cognitive errors. This pattern shifts notably with the introduction of ChatGPT models, which tend to respond correctly, avoiding the traps embedded in the tasks. Both ChatGPT-3.5 and 4 utilize the input-output context window to engage in chain-of-thought reasoning, reminiscent of how people use notepads to support their system 2 thinking. Yet, they remain accurate even when prevented from engaging in chain-of-thought reasoning, indicating that their system-1-like next-word generation processes are more accurate than those of older models. Our findings highlight the value of applying psychological methodologies to study large language models, as this can uncover previously undetected emergent characteristics.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Human and LLM performance on the CRT tasks.
a, Exemplary responses to one of the CRT tasks, categorized as correct, intuitive (but incorrect) and atypical (that is, all other incorrect responses). Within each category, the responses that were preceded by written chain-of-thought reasoning were additionally labeled as ‘chain-of-thought responses’. b, Human and LLM performance on 150 CRT tasks. c, LLMs’ responses when instructed to engage or prevented from engaging in chain-of-thought reasoning. The data source file includes 95% confidence intervals. Source data
Fig. 2
Fig. 2. Human and LLM performance on semantic illusions.
a. Exemplary responses to one of the semantic illusions, categorized as correct, intuitive and atypical. b, Human and LLM performance on 50 semantic illusions. c, GPT-3-davinci-003’s responses when instructed to examine the task’s assumptions. The data source file includes 95% confidence intervals. Source data
Extended Data Fig. 1
Extended Data Fig. 1. Learning curves.
Change in the fraction of GPT-3-davinci-003’s correct responses against the number of training examples that the task was prefixed with. Error bars represent 95% confidence intervals. Source data
See this image and copyright information in PMC

References

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