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Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies

A zero-shot evaluation of three leading commercial large language models on fine-grained emotion taxonomies.

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Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies

By Lawrence Obiuwevwi, Krzysztof J. Rechowicz, Jessica M. Johnson, Vikas Ashok, Sachin Shetty, Sampath JayarathnaarXiv
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This paper presents a unified zero-shot evaluation of three LLMs (Claude, ChatGPT, and Gemini) on a 13-class emotion classification task. The results show that all models excel on certain emotions but consistently fail on others.

The study highlights the limitations of current AI systems in fine-grained emotion classification.

Abstract

This paper presents a unified zero-shot evaluation of three LLMs (Claude, ChatGPT, and Gemini) on a 13-class emotion classification task. The results show that all models excel on certain emotions but consistently fail on others. The study highlights the limitations of current AI systems in fine-grained emotion classification.

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emotion recognitionaffective computingzero-shot evaluationlarge language modelsfine-grained emotion taxonomiesLarge Language ModelsAI AgentsOntology & TaxonomySemantic Interoperability
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Quantifying the Affective Gap: A Zero-Shot Evaluation of LLMs on Fine-Grained Emotion Taxonomies | Aramai