mlsanityData to 14 Oct 2026

Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations

arXiv 2405.11100 ↗First posted 17 May 2024cs.AI cs.CLcited by 19

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Abstract (arXiv)

Large language models (LLMs) have taken centre stage in debates on Artificial Intelligence. Yet there remains a gap in how to assess LLMs' conformity to important human values. In this paper, we investigate whether state-of-the-art LLMs, GPT-4 and Claude 2.1 (Gemini Pro and LLAMA 2 did not generate valid results) are moral hypocrites. We employ two research instruments based on the Moral Foundations Theory: (i) the Moral Foundations Questionnaire (MFQ), which investigates which values are considered morally relevant in abstract moral judgements; and (ii) the Moral Foundations Vignettes (MFVs), which evaluate moral cognition in concrete scenarios related to each moral foundation. We characterise conflicts in values between these different abstractions of moral evaluation as hypocrisy. We found that both models displayed reasonable consistency within each instrument compared to humans, but they displayed contradictory and hypocritical behaviour when we compared the abstract values present in the MFQ to the evaluation of concrete moral violations of the MFV.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 8 of its 45 references that are in the map. First posted on arXiv 17 May 2024.

Builds on

8 of 45 references.

References · 8

  1. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  2. 2023Towards Understanding Sycophancy in Language Modelscited by 1,040
  3. 2020Artificial Intelligence, Values and Alignmentcited by 448
  4. 2022Selective Annotation Makes Language Models Better Few-Shot Learnerscited by 243
  5. 2023Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learningcited by 149
  6. 2022Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identitycited by 74
  7. 2017Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviorscited by 14
  8. 2023Truth Machines: Synthesizing Veracity in AI Language Modelscited by 8

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 7; 2026: 10.

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  1. 2024Evaluating Gender Bias of LLMs in Making Morality Judgementscited by 15
  2. 2025Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLMscited by 10
  3. 2025Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Moralitycited by 9
  4. 2026Tracing Moral Foundations in Large Language Modelscited by 6
  5. 2025Auditing the Ethical Logic of Generative AI Modelscited by 5
  6. 2025Moral Susceptibility and Robustness under Persona Role-Play in Large Language Modelscited by 4
  7. 2024A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challengescited by 3
  8. 2025Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theorycited by 3
  9. 2025Differences in the Moral Foundations of Large Language Modelscited by 3
  10. 2026On the Credibility of Evaluating LLMs using Survey Questionscited by 3
  11. 2026On the Context Sensitivity of LLM Moral Judgmentcited by 3
  12. 2026Do Emotions Influence Moral Judgment in Large Language Models?cited by 2
7 more
  1. 2026Incoherent Values? Probing LLM Preferences Through Parametric Variationcited by 2
  2. 2025IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimizationcited by 1
  3. 2026Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmascited by 1
  4. 2026Persona-Model Collapse in Emergent Misalignmentcited by 1
  5. 2026The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgmentcited by 1
  6. 2026Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learningcited by 0
  7. 2026Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picturecited by 0