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This content will become publicly available on September 1, 2025

Title: From Text to Emotion: Unveiling the Emotion Annotation Capabilities of LLMs
Training emotion recognition models has relied heavily on human annotated data, which present diversity, quality, and cost challenges. In this paper, we explore the potential of Large Language Models (LLMs), specifically GPT-4, in automating or assisting emotion annotation. We compare GPT-4 with supervised models and/or humans in three aspects: agreement with human annotations, alignment with human perception, and impact on model training. We find that common metrics that use aggregated human annotations as ground truth can underestimate GPT-4's performance, and our human evaluation experiment reveals a consistent preference for GPT-4 annotations over humans across multiple datasets and evaluators. Further, we investigate the impact of using GPT-4 as an annotation filtering process to improve model training. Together, our findings highlight the great potential of LLMs in emotion annotation tasks and underscore the need for refined evaluation methodologies.  more » « less
Award ID(s):
2230172
PAR ID:
10536399
Author(s) / Creator(s):
; ;
Publisher / Repository:
Interspeech
Date Published:
Format(s):
Medium: X
Location:
Kos, Greece
Sponsoring Org:
National Science Foundation
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