paper-with-me

홈 › Papers

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

2026-04-18 · Sree Bhattacharyya, Manas Mehta, Leona Chen, Cristina Salvador, Agata Lapedriza, Shiran Dudy, James Z. Wang 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.

📄 PDF Abstract BibTeX arXiv:2604.16757

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

The Many Faces of Anger: A Multicultural Video Dataset of Negative Emotions in the Wild (MFA-Wild)

2021-12-10 · Roya Javadi, Angelica Lim

The portrayal of negative emotions such as anger can vary widely between cultures and contexts, depending on the acceptability of expressing full-blown emotions rather than suppression to maintain harmony. The majority o…

Cultural Vocal Bursts Intensity PredictionEmotion ClassificationEmotion Recognition

TeamCEN at SemEval-2018 Task 1: Global Vectors Representation in Emotion Detection

2018-06-01 · SEMEVAL 2018 6 · Anon George, Barathi Ganesh H. B., An Kumar M, 외

Emotions are a way of expressing human sentiments. In the modern era, social media is a platform where we convey our emotions. These emotions can be joy, anger, sadness and fear. Understanding the emotions from the writt…

Dimensionality ReductionGeneral Classificationtext-classificationText Classification

Detect Depression from Social Networks with Sentiment Knowledge Sharing

2023-06-13 · Yan Shi, Yao Tian, Chengwei Tong, Chunyan Zhu 외

Social network plays an important role in propagating people's viewpoints, emotions, thoughts, and fears. Notably, following lockdown periods during the COVID-19 pandemic, the issue of depression has garnered increasing …

Depression Detection

Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety

2021-06-01 · JEP/TALN/RECITAL 2021 6 · Mohamed Amine Romdhane, Elena Cabrio, Serena Villata

Sifting French Tweets to Investigate the Impact of Covid-19 in Triggering Intense Anxiety. Social media can be leveraged to understand public sentiment and feelings in real-time, and target public health messages based o…

Large language models show fragile cognitive reasoning about human emotions

2025-08-07 · Sree Bhattacharyya, Evgenii Kuriabov, Lucas Craig, Tharun Dilliraj 외 arxiv

Affective computing seeks to support the holistic development of artificial intelligence by enabling machines to engage with human emotion. Recent foundation models, particularly large language models (LLMs), have been t…