LLM emotion labeling of dispute dialogues explains up to ~40% of variance in subjective outcomes (vs ~5% in prior negotiation work) and reveals anger escalation and compassion de-escalation patterns.
Multilingual Language Models are not Multicultural: A Case Study in Emotion
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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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Emotionally-Aware Agents for Dispute Resolution
LLM emotion labeling of dispute dialogues explains up to ~40% of variance in subjective outcomes (vs ~5% in prior negotiation work) and reveals anger escalation and compassion de-escalation patterns.