Exploring the potential of large language models to understand interpersonal emotion regulation strategies from narratives.
Year: 2025
Type: article
Abstract: = 2090), using both ChatGPT and Claude. Additional evaluations of LLMs' performance using different accuracy metrics pointed to notable variability in LLM's capability when interpreting narratives across different emotions and regulatory strategies. These results point to the strengths and limitations of LLMs in classifying regulation strategies, and the importance of prompt engineering and validation. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
Source: Emotion
DOI: 10.1037/emo0001528
Authors: Belén López-Pérez, Yuhui Chen, Xiuhui Li, Shixing Cheng, Pooya Razavi
Institutions: University of Manchester, University of Oregon
Language: English
Topic: Emotion and Mood Recognition
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4409541180