mlsanityData to 7 Oct 2026

Large Language Models Do Not Simulate Human Psychology

arXiv 2508.06950 ↗First posted 9 Aug 2025cs.AIcited by 17

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Abstract (arXiv)

Large Language Models (LLMs),such as ChatGPT, are increasingly used in research, ranging from simple writing assistance to complex data annotation tasks. Recently, some research has suggested that LLMs may even be able to simulate human psychology and can, hence, replace human participants in psychological studies. We caution against this approach. We provide conceptual arguments against the hypothesis that LLMs simulate human psychology. We then present empiric evidence illustrating our arguments by demonstrating that slight changes to wording that correspond to large changes in meaning lead to notable discrepancies between LLMs' and human responses, even for the recent CENTAUR model that was specifically fine-tuned on psychological responses. Additionally, different LLMs show very different responses to novel items, further illustrating their lack of reliability. We conclude that LLMs do not simulate human psychology and recommend that psychological researchers should treat LLMs as useful but fundamentally unreliable tools that need to be validated against human responses for every new application.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). Builds on 17 of its 45 references that are in the map. First posted on arXiv 9 Aug 2025.

Builds on

17 of 45 references.

References · 17

  1. 2022Training language models to follow instructions with human feedbackcited by 15,712
  2. 2023Generative Agents: Interactive Simulacra of Human Behaviorcited by 3,500
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  4. 2023Bias and Fairness in Large Language Models: A Surveycited by 626
  5. 2023Instruction Tuning for Large Language Models: A Surveycited by 617
  6. 2024LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individualscited by 235
  7. 2023Evaluating Large Language Models in Theory of Mind Taskscited by 217
  8. 2023Language models are not naysayers: An analysis of language models on negation benchmarkscited by 73
  9. 2023Exploring the psychology of LLMs' Moral and Legal Reasoningcited by 43
  10. 2024Evaluating Cultural Adaptability of a Large Language Model via Simulation of Synthetic Personascited by 36
  11. 2025LLM-based Human Simulations Have Not Yet Been Reliablecited by 30
  12. 2024How Reliable is Your Simulator? Analysis on the Limitations of Current LLM-based User Simulators for Conversational Recommendationcited by 25
5 more
  1. 2025Re-evaluating Theory of Mind evaluation in large language modelscited by 23
  2. 2024Attributions toward Artificial Agents in a modified Moral Turing Testcited by 18
  3. 2024The Moral Turing Test: Evaluating Human-LLM Alignment in Moral Decision-Makingcited by 12
  4. 2024Can Machines Think Like Humans? A Behavioral Evaluation of LLM Agents in Dictator Gamescited by 9
  5. 2022Fine-Grained Detection of Solidarity for Women and Migrants in 155 Years of German Parliamentary Debatescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 4; 2026: 13.

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  1. 2025Moloch's Bargain: Emergent Misalignment When LLMs Compete for Audiencescited by 7
  2. 2026Take Out Your Calculators: Estimating the Real Difficulty of Question Items with LLM Student Simulationscited by 5
  3. 2025Addressing Longstanding Challenges in Cognitive Science with Language Modelscited by 3
  4. 2025Can Finetuing LLMs on Small Human Samples Increase Heterogeneity, Alignment, and Belief-Action Coherence?cited by 2
  5. 2026Dynamic Personality Adaptation in Large Language Models via State Machinescited by 2
  6. 2026The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisionscited by 2
  7. 2026HumanLLM: Benchmarking and Improving LLM Anthropomorphism via Human Cognitive Patternscited by 1
  8. 2026GTA: Generative Traffic Agents for Simulating Realistic Mobility Behaviorcited by 1
  9. 2026LLM surprisal is necessary but not sufficient to capture English garden-path effects: Evidence from joint latent modeling of reading paradigmscited by 1
  10. 2026Interview-Informed Generative Agents for Product Discovery: A Validation Studycited by 1
  11. 2026LifeSide: Benchmarking Agents as Lifelong Digital Companionscited by 1
  12. 2025Measure what Matters: Psychometric Evaluation of AI with Situational Judgment Testscited by 0
5 more
  1. 2026The Third Ambition: Artificial Intelligence and the Science of Human Behaviorcited by 0
  2. 2026The Storyteller in the Model: Narrative Pattern Inheritance, Escalation Dynamics, and Alignment Governance in LLMscited by 0
  3. 2026Position: We Need Practical AI Alignment Methods to Mirror Human Reasoningcited by 0
  4. 2026Process-Constituted Intelligence: A Shared Criterion for Humans and Machinescited by 0
  5. 2026Aligning with Lived Experience: Heterogeneous Benefits of Fine Tuning in Mental Health Support Generationcited by 0