Human evaluators preferred GPT-4's zero-shot emotion labels over original human labels in 62% of disagreement samples, and GPT-4 pre-filtering and post-filtering can reduce annotation workload and improve training efficiency.
Affective computing in education: A systematic review and future research,
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Rethinking Emotion Annotations in the Era of Large Language Models
Human evaluators preferred GPT-4's zero-shot emotion labels over original human labels in 62% of disagreement samples, and GPT-4 pre-filtering and post-filtering can reduce annotation workload and improve training efficiency.