Multilingual Language Models are not Multicultural: A Case Study in Emotion

Shreya Havaldar, Sunny Rai, Bhumika Singhal, Langchen Liu, Sharath Chandra Guntuku +1 more
7/3/2023
cs.CL

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Abstract

Emotions are experienced and expressed differently across the world. In order to use Large Language Models (LMs) for multilingual tasks that require emotional sensitivity, LMs must reflect this cultural variation in emotion. In this study, we investigate whether the widely-used multilingual LMs in 2023 reflect differences in emotional expressions across cultures and languages. We find that embeddings obtained from LMs (e.g., XLM-RoBERTa) are Anglocentric, and generative LMs (e.g., ChatGPT) reflect Western norms, even when responding to prompts in other languages. Our results show that multilingual LMs do not successfully learn the culturally appropriate nuances of emotion and we highlight possible research directions towards correcting this.

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Cite this paper

@article{havaldar2023multilingual,
  title  = {Multilingual Language Models are not Multicultural: A Case Study in Emotion},
  author = {Shreya Havaldar and Sunny Rai and Bhumika Singhal and Langchen Liu and Sharath Chandra Guntuku and Lyle Ungar},
  year   = {2023},
  eprint = {2307.01370},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2307.01370v2}
}

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