(Ir)rationality and Cognitive Biases in Large Language Models

• URL: http://arxiv.org/abs/2402.09193v2
• Date: Thu, 15 Feb 2024 11:09:09 GMT
• Title: (Ir)rationality and Cognitive Biases in Large Language Models
• Authors: Olivia Macmillan-Scott and Mirco Musolesi
• Abstract summary: We evaluate seven language models using tasks from the cognitive psychology literature. We find that, like humans, LLMs display irrationality in these tasks. When incorrect answers are given by LLMs to these tasks, they are often incorrect in ways that differ from human-like biases.
• Score: 2.9008806248012333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Do large language models (LLMs) display rational reasoning? LLMs have been shown to contain human biases due to the data they have been trained on; whether this is reflected in rational reasoning remains less clear. In this paper, we answer this question by evaluating seven language models using tasks from the cognitive psychology literature. We find that, like humans, LLMs display irrationality in these tasks. However, the way this irrationality is displayed does not reflect that shown by humans. When incorrect answers are given by LLMs to these tasks, they are often incorrect in ways that differ from human-like biases. On top of this, the LLMs reveal an additional layer of irrationality in the significant inconsistency of the responses. Aside from the experimental results, this paper seeks to make a methodological contribution by showing how we can assess and compare different capabilities of these types of models, in this case with respect to rational reasoning.

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