mlsanityData to 7 Oct 2026

LLM Cognitive Judgements Differ From Human

arXiv 2307.11787 ↗First posted 20 Jul 2023cs.CL cs.AI cs.LGcited by 8

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Abstract (arXiv)

Large Language Models (LLMs) have lately been on the spotlight of researchers, businesses, and consumers alike. While the linguistic capabilities of such models have been studied extensively, there is growing interest in investigating them as cognitive subjects. In the present work I examine GPT-3 and ChatGPT capabilities on an limited-data inductive reasoning task from the cognitive science literature. The results suggest that these models' cognitive judgements are not human-like.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 20 Jul 2023.

Builds on

15 of 30 references.

References · 15

  1. 2020Language Models are Few-Shot Learnerscited by 32,199
  2. 2022Training language models to follow instructions with human feedbackcited by 15,712
  3. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  4. 2020Beyond Accuracy: Behavioral Testing of NLP models with CheckListcited by 979
  5. 2018Neural Network Acceptability Judgmentscited by 952
  6. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  7. 2022Using cognitive psychology to understand GPT-3cited by 389
  8. 2019What BERT is not: Lessons from a new suite of psycholinguistic diagnostics for language modelscited by 317
  9. 2018Colorless green recurrent networks dream hierarchicallycited by 210
  10. 2023A Categorical Archive of ChatGPT Failurescited by 167
  11. 2019Neural Language Models as Psycholinguistic Subjects: Representations of Syntactic Statecited by 137
  12. 2023Do large language models resemble humans in language use?cited by 34
3 more
  1. 2023Does ChatGPT have Theory of Mind?cited by 16
  2. 2023Evaluating statistical language models as pragmatic reasonerscited by 14
  3. 2023Does Conceptual Representation Require Embodiment? Insights From Large Language Modelscited by 5

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 5; 2025: 2.

8 papers.

  1. 2024Large Language Models and Cognitive Science: A Comprehensive Review of Similarities, Differences, and Challengescited by 48
  2. 2024(Ir)rationality and Cognitive Biases in Large Language Modelscited by 23
  3. 2023Using Artificial Populations to Study Psychological Phenomena in Neural Modelscited by 8
  4. 2024Large Language Model Recall Uncertainty is Modulated by the Fan Effectcited by 7
  5. 2025Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive zijicited by 2
  6. 2024From Words to Worlds: Compositionality for Cognitive Architecturescited by 1
  7. 2024Meaningless is better: hashing bias-inducing words in LLM prompts improves performance in logical reasoning and statistical learningcited by 1
  8. 2025The Anatomy of Speech Persuasion: Linguistic Shifts in LLM-Modified Speechescited by 0