ArticleRoyal Society open science2024
(Ir)rationality and cognitive biases in large language models.
Article in Royal Society open science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Evidence-to-Decision Frameworks: Enhancing the Quality and Rigour of Guidelines and Recommendations.Clinical and public health guidelines · 2026Article
- Benchmarking Large Language Model Rationality Using Measurement Axioms.Research square · 2026Article
- Legal Decision Biases in GPT: A Comparison with Human Judgment.Behavioral sciences (Basel, Switzerland) · 2026Article
- Disagreement between human and AI evaluation of treatment plans.Scientific reports · 2026Article
- Divergent patterns of probabilistic reasoning in humans and GPT-5.Frontiers in psychology · 2026Article
- Cross-linguistic patterns of cognitive biases in large language models: a comparative study in English, Hebrew, and Russian.Frontiers in artificial intelligence · 2026Article
- Biases, evolutionary mismatch and the comparative analysis of human versus artificial cognition: a comment on Macmillan-Scott and Musolesi (2024).Royal Society open science · 2025Article
- Moving LLM evaluation forward: lessons from human judgment research.Frontiers in artificial intelligence · 2025Article
- Will multimodal large language models ever achieve deep understanding of the world?Frontiers in systems neuroscience · 2025Article
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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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