Exploring the potential of large language models to understand interpersonal emotion regulation strategies from narratives.

Abstract

Interpersonal emotion regulation involves using diverse strategies to influence others' emotions, commonly assessed with questionnaires. However, this method may be less effective for individuals with limited literacy or introspection skills. To address this, recent studies have adopted narrative-based approaches, though these require time-intensive qualitative analysis. Given the potential of artificial intelligence (AI) and large language models (LLM) for information classification, we evaluated the feasibility of using AI to categorize interpersonal emotion regulation strategies. We conducted two studies in which we compared AI performance against human coding in identifying regulation strategies from narrative data. In Study 1, with 2,824 responses, ChatGPT initially achieved Kappa values over .47. Refinements in prompts (i.e., coding instructions) led to improved consistency between ChatGPT and human coders (κ > .79). In Study 2, the refined prompts demonstrated comparable accuracy (κ > .76) when analyzing a new set of responses (N = 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).