LLM Plus Machine Learning Outperform Expert Rating to Predict Life Satisfaction From Self-Statement Text

Abstract

This study explores an innovative approach to predicting individual life satisfaction by combining large language models (LLMs) with machine learning (ML) techniques. Traditional life satisfaction assessments rely on self-report questionnaires, which can be time-consuming and resource intensive. To address these limitations, we developed a method that utilizes LLMs for feature extraction from open-ended self-statement texts, followed by ML prediction. We compared this approach with standalone LLM predictions and expert ratings. A sample of 378 participants completed the satisfaction with life scale (SWLS) and wrote self-statements about their current life situation. The LLM-based ML model, using a LightGBM regressor, achieved a correlation of 0.542 with self-reported SWLS scores, outperforming both the standalone LLM (r $=$ 0.491) and expert ratings (r $=$ 0.455). Effect size analysis revealed a statistically significant moderate effect size difference between the LLM-based ML model and expert ratings (Cohen's d $=$ 0.499, 95% CI [0.043, 0.955]). These findings demonstrate the potential of integrating LLM and ML for an efficient and accurate assessment of life satisfaction, challenging conventional methods, and opening new avenues for psychological measurement. The study's implications extend to research, clinical practice, and policymaking, offering promising advancements in AI-assisted psychological assessment.