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

arXiv: https://arxiv.org/abs/2405.11100 · HTML: https://mlsanity.com/paper/2405.11100/

## 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 (most cited first, 8 of 8)

- [Sparks of Artificial General Intelligence: Early experiments with GPT-4](https://mlsanity.com/paper/2303.12712/) (2023)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Artificial Intelligence, Values and Alignment](https://mlsanity.com/paper/2001.09768/) (2020)
- [Selective Annotation Makes Language Models Better Few-Shot Learners](https://mlsanity.com/paper/2209.01975/) (2022)
- [Large Language Models Are Latent Variable Models: Explaining and Finding Good Demonstrations for In-Context Learning](https://mlsanity.com/paper/2301.11916/) (2023)
- [Moral Mimicry: Large Language Models Produce Moral Rationalizations Tailored to Political Identity](https://mlsanity.com/paper/2209.12106/) (2022)
- [Predicting Demographics, Moral Foundations, and Human Values from Digital Behaviors](https://mlsanity.com/paper/1712.01930/) (2017)
- [Truth Machines: Synthesizing Veracity in AI Language Models](https://mlsanity.com/paper/2301.12066/) (2023)

## Built on by (most cited first, 19 of 19)

- [Evaluating Gender Bias of LLMs in Making Morality Judgements](https://mlsanity.com/paper/2410.09992/) (2024)
- [Discerning What Matters: A Multi-Dimensional Assessment of Moral Competence in LLMs](https://mlsanity.com/paper/2506.13082/) (2025)
- [Do Psychometric Tests Work for Large Language Models? Evaluation of Tests on Sexism, Racism, and Morality](https://mlsanity.com/paper/2510.11254/) (2025)
- [Tracing Moral Foundations in Large Language Models](https://mlsanity.com/paper/2601.05437/) (2026)
- [Auditing the Ethical Logic of Generative AI Models](https://mlsanity.com/paper/2504.17544/) (2025)
- [Moral Susceptibility and Robustness under Persona Role-Play in Large Language Models](https://mlsanity.com/paper/2511.08565/) (2025)
- [A Survey on Moral Foundation Theory and Pre-Trained Language Models: Current Advances and Challenges](https://mlsanity.com/paper/2409.13521/) (2024)
- [Investigating Political and Demographic Associations in Large Language Models Through Moral Foundations Theory](https://mlsanity.com/paper/2510.13902/) (2025)
- [Differences in the Moral Foundations of Large Language Models](https://mlsanity.com/paper/2511.11790/) (2025)
- [On the Credibility of Evaluating LLMs using Survey Questions](https://mlsanity.com/paper/2602.04033/) (2026)
- [On the Context Sensitivity of LLM Moral Judgment](https://mlsanity.com/paper/2603.23114/) (2026)
- [Do Emotions Influence Moral Judgment in Large Language Models?](https://mlsanity.com/paper/2604.19125/) (2026)
- [Incoherent Values? Probing LLM Preferences Through Parametric Variation](https://mlsanity.com/paper/2606.21102/) (2026)
- [IROTE: Human-like Traits Elicitation of Large Language Model via In-Context Self-Reflective Optimization](https://mlsanity.com/paper/2508.08719/) (2025)
- [Growth First, Care Second? Tracing the Landscape of LLM Value Preferences in Everyday Dilemmas](https://mlsanity.com/paper/2602.04456/) (2026)
- [Persona-Model Collapse in Emergent Misalignment](https://mlsanity.com/paper/2605.12850/) (2026)
- [The yes-no bias of large language models reflects answer order and wording, not shifts in moral judgment](https://mlsanity.com/paper/2607.05552/) (2026)
- [Profiling learners' affective engagement: Emotion AI, intercultural pragmatics, and language learning](https://mlsanity.com/paper/2603.20479/) (2026)
- [Position: Evaluations of AI Moral Reasoning Still Miss Half of the Picture](https://mlsanity.com/paper/2608.14566/) (2026)

## 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.