mlsanityData to 7 Oct 2026

(Ir)rationality and Cognitive Biases in Large Language Models

arXiv 2402.09193 ↗First posted 14 Feb 2024cs.CL cs.AI cs.HCcited by 23

On the map: LLM reasoning → Reasoning benchmarks and evaluation

Abstract (arXiv)

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.

Key facts. Cited by 23 papers in the map (data through 7 Oct 2026). Placed in Reasoning benchmarks and evaluation, one of 17 directions in LLM reasoning. Builds on 26 of its 49 references that are in the map. First posted on arXiv 14 Feb 2024.

Builds on

26 of 49 references.

Closest ancestors · 5

Same direction, newest first

  1. 2023Inductive reasoning in humans and large language models11 Jun
  2. 2023Turning large language models into cognitive models6 Jun
  3. 2022Emergent Analogical Reasoning in Large Language Models19 Dec
  4. 2022Thinking Fast and Slow in Large Language Models10 Dec
  5. 2022Language models show human-like content effects on reasoning tasks14 Jul

Other references · 21

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  3. 2023Reflexion: Language Agents with Verbal Reinforcement Learningcited by 4,064
  4. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  6. 2022LaMDA: Language Models for Dialog Applicationscited by 1,218
  7. 2023Whose Opinions Do Language Models Reflect?cited by 759
  8. 2023XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Modelscited by 477
  9. 2022Using cognitive psychology to understand GPT-3cited by 389
  10. 2022Can language models learn from explanations in context?cited by 248
  11. 2021Large Pre-trained Language Models Contain Human-like Biases of What is Right and Wrong to Docited by 178
  12. 2023In-Context Impersonation Reveals Large Language Models' Strengths and Biasescited by 154
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  1. 2023Language Model Behavior: A Comprehensive Surveycited by 92
  2. 2024Escalation Risks from Language Models in Military and Diplomatic Decision-Makingcited by 61
  3. 2022The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMscited by 57
  4. 2023Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Biascited by 51
  5. 2022Can language models handle recursively nested grammatical structures? A case study on comparing models and humanscited by 41
  6. 2023Does ChatGPT have Theory of Mind?cited by 16
  7. 2023LLM Cognitive Judgements Differ From Humancited by 8
  8. 2022BIASeD: Bringing Irrationality into Automated System Designcited by 5
  9. 2023(Ir)rationality in AI: State of the Art, Research Challenges and Open Questionscited by 5

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 11; 2025: 5; 2026: 7.

23 papers.

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  1. 2024Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challengescited by 48
  2. 2024Moral Alignment for LLM Agentscited by 39
  3. 2024Anchoring Bias in Large Language Models: An Experimental Studycited by 37
  4. 2024A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directionscited by 27
  5. 2024Cognitive Biases in Large Language Models: A Survey and Mitigation Experimentscited by 20
  6. 2024Large Language Model Benchmarks in Medical Taskscited by 16
  7. 2024Explaining Large Language Models Decisions Using Shapley Valuescited by 12
  8. 2025Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspectivecited by 12
  9. 2024MindScope: Exploring cognitive biases in large language models through Multi-Agent Systemscited by 5
  10. 2025LLM Agents Display Human Biases but Exhibit Distinct Learning Patternscited by 3
  11. 2024Heuristics and Biases in AI Decision-Making: Implications for Responsible AGIcited by 2
  12. 2026Why Do LLMs Struggle in Strategic Play? Broken Links Between Observations, Beliefs, and Actionscited by 2
11 more
  1. 2024Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learningcited by 1
  2. 2025Predicting Human Choice Between Textually Described Lotteriescited by 1
  3. 2025Applying Psychometrics to Large Language Model Simulated Populations: Recreating the HEXACO Personality Inventory Experiment with Generative Agentscited by 1
  4. 2026DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmarkcited by 1
  5. 2024People will agree what I think: Investigating LLM's False Consensus Effectcited by 0
  6. 2025From General Reasoning to Domain Expertise: Uncovering the Limits of Generalization in Large Language Modelscited by 0
  7. 2026Cooperate to Compete: Strategic Coordination in Multi-Agent Conquestcited by 0
  8. 2026How reliable are LLMs when it comes to playing dice?cited by 0
  9. 2026Words Speak Louder Than Code: Investigating Cognitive Heuristics in LLM-Based Code Vulnerability Detectioncited by 0
  10. 2026A Dual-Process Perspective on Nudge Susceptibility in LLM-Based GUI Agentscited by 0
  11. 2026ContextAdapt: Evaluating Contextual Adaptation and Value Alignment in LLMscited by 0

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  1. 2024CBEval: A framework for evaluating and interpreting cognitive biases in LLMs
  2. 2023Evaluating Large Language Models with NeuBAROCO: Syllogistic Reasoning Ability and Human-like Biases
  3. 2023A Systematic Comparison of Syllogistic Reasoning in Humans and Language Models
  4. 2024Exploring Reasoning Biases in Large Language Models Through Syllogism: Insights from the NeuBAROCO Dataset
  5. 2025Large Language Models are overconfident and amplify human bias
  6. 2026Sparks of Rationality: Do Reasoning LLMs Align with Human Judgment and Choice?
  7. 2024Do Large Language Models Truly Grasp Mathematics? An Empirical Exploration From Cognitive Psychology
  8. 2024A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners