# Expressing Social Emotions: Misalignment Between LLMs and Human Cultural Emotion Norms

arXiv: https://arxiv.org/abs/2604.16757 · HTML: https://mlsanity.com/paper/2604.16757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 75 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 25 of 25)

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities](https://mlsanity.com/paper/2507.06261/) (2025)
- [Towards Understanding Sycophancy in Language Models](https://mlsanity.com/paper/2310.13548/) (2023)
- [Phi-4 Technical Report](https://mlsanity.com/paper/2412.08905/) (2024)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [Cultural Bias and Cultural Alignment of Large Language Models](https://mlsanity.com/paper/2311.14096/) (2023)
- [Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond)](https://mlsanity.com/paper/2510.22954/) (2025)
- [Generative Language Models Exhibit Social Identity Biases](https://mlsanity.com/paper/2310.15819/) (2023)
- [Is Temperature the Creativity Parameter of Large Language Models?](https://mlsanity.com/paper/2405.00492/) (2024)
- [Multilingual Language Models are not Multicultural: A Case Study in Emotion](https://mlsanity.com/paper/2307.01370/) (2023)
- [LLM Voting: Human Choices and AI Collective Decision Making](https://mlsanity.com/paper/2402.01766/) (2024)
- [Diminished Diversity-of-Thought in a Standard Large Language Model](https://mlsanity.com/paper/2302.07267/) (2023)
- [Multilingual != Multicultural: Evaluating Gaps Between Multilingual Capabilities and Cultural Alignment in LLMs](https://mlsanity.com/paper/2502.16534/) (2025)
- [Evaluating Vision-Language Models for Emotion Recognition](https://mlsanity.com/paper/2502.05660/) (2025)
- [CULEMO: Cultural Lenses on Emotion -- Benchmarking LLMs for Cross-Cultural Emotion Understanding](https://mlsanity.com/paper/2503.10688/) (2025)
- [Divine LLaMAs: Bias, Stereotypes, Stigmatization, and Emotion Representation of Religion in Large Language Models](https://mlsanity.com/paper/2407.06908/) (2024)
- [The Strong Pull of Prior Knowledge in Large Language Models and Its Impact on Emotion Recognition](https://mlsanity.com/paper/2403.17125/) (2024)
- [Analyzing Cultural Representations of Emotions in LLMs through Mixed Emotion Survey](https://mlsanity.com/paper/2408.02143/) (2024)
- [Strategic Algorithmic Monoculture: Experimental Evidence from Coordination Games](https://mlsanity.com/paper/2604.09502/) (2026)
- [Social Norms in Cinema: A Cross-Cultural Analysis of Shame, Pride and Prejudice](https://mlsanity.com/paper/2402.11333/) (2024)
- [Beyond Context to Cognitive Appraisal: Emotion Reasoning as a Theory of Mind Benchmark for Large Language Models](https://mlsanity.com/paper/2506.00334/) (2025)
- [CulTrace: Tracing Internal Cultural Reasoning in Large Language Models](https://mlsanity.com/paper/2508.08879/) (2025)
- [Tears or Cheers? Benchmarking LLMs via Culturally Elicited Distinct Affective Responses](https://mlsanity.com/paper/2601.13024/) (2026)
- [Emotion-LLaMAv2 and MMEVerse: A New Framework and Benchmark for Multimodal Emotion Understanding](https://mlsanity.com/paper/2601.16449/) (2026)
- [From Anger to Joy: How Nationality Personas Shape Emotion Attribution in Large Language Models](https://mlsanity.com/paper/2506.02431/) (2025)

## Built on by (most cited first, 1 of 1)

- [The Judgment-Consequence Gap: LLM Moral Reasoning in Healthcare Decisions](https://mlsanity.com/paper/2608.05583/) (2026)

## Abstract (arXiv)

The expression of emotions that serve social purposes, such as asserting independence or fostering interdependence, is central to human interactions and varies systematically across cultures. As LLMs are increasingly used to simulate human behavior in culturally nuanced interactions, it is important to understand whether they faithfully capture human patterns of social emotion expression. When LLM responses are not culturally aligned, their utility is compromised -- particularly when users assume they are interacting with a culturally attuned interlocutor, and may act on advice that proves inappropriate in their cultural context. We present a psychologically informed evaluation framework of cross-cultural social emotion expression in LLMs. Using a human study comparing European American and Latin American participants' expression of engaging and disengaging emotions, we evaluate six frontier LLMs on their ability to reflect culturally differentiated patterns for expressing social emotions. We find systematic misalignment between model and human behavior: all models express engaging emotions more than disengaging ones, with particularly stark differences observed for the generally well-represented European American persona. We further highlight that LLM responses are highly concentrated and deterministic, failing to capture the diversity of human responses in expressing social emotions. Our ablation analyses reveal that these patterns are robust to sampling temperatures, partially sensitive to prompt language, and dependent on the response elicitation format. Together, our findings highlight limitations in how current LLMs represent the interaction of cultural and emotional axes, particularly when expressing social emotions, with direct implications for their deployment in cross-cultural affective contexts.