(Ir)rationality and Cognitive Biases in Large Language Models
This work addresses the problem of understanding LLM reasoning capabilities for researchers and practitioners, though it is incremental in extending bias analysis to rationality.
The paper investigates whether large language models (LLMs) exhibit rational reasoning by evaluating seven models on cognitive psychology tasks, finding that they display irrationality but in ways that differ from human biases and with significant response inconsistency.
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.