# Do Emotions Influence Moral Judgment in Large Language Models?

arXiv: https://arxiv.org/abs/2604.19125 · HTML: https://mlsanity.com/paper/2604.19125/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 48 references that are in the map.
- First posted on arXiv 21 Apr 2026.

## Builds on (most cited first, 26 of 26)

- [Aligning AI With Shared Human Values](https://mlsanity.com/paper/2008.02275/) (2020)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [A Corpus and Evaluation Framework for Deeper Understanding of Commonsense Stories](https://mlsanity.com/paper/1604.01696/) (2016)
- [LexGLUE: A Benchmark Dataset for Legal Language Understanding in English](https://mlsanity.com/paper/2110.00976/) (2021)
- [Sentiment Analysis in the Era of Large Language Models: A Reality Check](https://mlsanity.com/paper/2305.15005/) (2023)
- [Social Chemistry 101: Learning to Reason about Social and Moral Norms](https://mlsanity.com/paper/2011.00620/) (2020)
- [Neural Theory-of-Mind? On the Limits of Social Intelligence in Large LMs](https://mlsanity.com/paper/2210.13312/) (2022)
- [Evaluating the Moral Beliefs Encoded in LLMs](https://mlsanity.com/paper/2307.14324/) (2023)
- [Large Language Models Understand and Can be Enhanced by Emotional Stimuli](https://mlsanity.com/paper/2307.11760/) (2023)
- [Can Machines Learn Morality? The Delphi Experiment](https://mlsanity.com/paper/2110.07574/) (2021)
- [Scruples: A Corpus of Community Ethical Judgments on 32,000 Real-Life Anecdotes](https://mlsanity.com/paper/2008.09094/) (2020)
- [The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems](https://mlsanity.com/paper/2204.03021/) (2022)
- [When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment](https://mlsanity.com/paper/2210.01478/) (2022)
- [Mechanistic Interpretability of Emotion Inference in Large Language Models](https://mlsanity.com/paper/2502.05489/) (2025)
- [Normative Evaluation of Large Language Models with Everyday Moral Dilemmas](https://mlsanity.com/paper/2501.18081/) (2025)
- [Whose Emotions and Moral Sentiments Do Language Models Reflect?](https://mlsanity.com/paper/2402.11114/) (2024)
- [Are Large Language Models Moral Hypocrites? A Study Based on Moral Foundations](https://mlsanity.com/paper/2405.11100/) (2024)
- [NegativePrompt: Leveraging Psychology for Large Language Models Enhancement via Negative Emotional Stimuli](https://mlsanity.com/paper/2405.02814/) (2024)
- [LLaMAs Have Feelings Too: Unveiling Sentiment and Emotion Representations in LLaMA Models Through Probing](https://mlsanity.com/paper/2505.16491/) (2025)
- [Are Rules Meant to be Broken? Understanding Multilingual Moral Reasoning as a Computational Pipeline with UniMoral](https://mlsanity.com/paper/2502.14083/) (2025)
- [Moral Persuasion in Large Language Models: Evaluating Susceptibility and Ethical Alignment](https://mlsanity.com/paper/2411.11731/) (2024)
- [GreedLlama: Performance of Financial Value-Aligned Large Language Models in Moral Reasoning](https://mlsanity.com/paper/2404.02934/) (2024)
- [Many LLMs Are More Utilitarian Than One](https://mlsanity.com/paper/2507.00814/) (2025)
- [MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMs](https://mlsanity.com/paper/2505.24423/) (2025)
- [Outraged AI: Large language models prioritise emotion over cost in fairness enforcement](https://mlsanity.com/paper/2510.17880/) (2025)
- [Anatomy of a Feeling: Narrating Embodied Emotions via Large Vision-Language Models](https://mlsanity.com/paper/2509.19595/) (2025)

## Built on by (most cited first, 2 of 2)

- [Emotional Labor Strategy Preferences in LLM Personas](https://mlsanity.com/paper/2609.00310/) (2026)
- [Chiaroscuro for Emotions: A Contrastive Emotion Benchmark Grounded in Appraisal Theory](https://mlsanity.com/paper/2609.03394/) (2026)

## Abstract (arXiv)

Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotions influence moral judgment remains underexplored. In this work, we develop an emotion-induction pipeline that infuses emotion into moral situations and evaluate shifts in moral acceptability across multiple datasets and LLMs. We observe a directional pattern: positive emotions increase moral acceptability and negative emotions decrease it, with effects strong enough to reverse binary moral judgments in up to 20% of cases, and with susceptibility scaling inversely with model capability. Our analysis further reveals that specific emotions can sometimes behave contrary to what their valence would predict (e.g., remorse paradoxically increases acceptability). A complementary human annotation study shows humans do not exhibit these systematic shifts, indicating an alignment gap in current LLMs.