The authors use LLMs in a zero-shot setup to predict psychological well-being scores (Ryff PWB) from a few minutes of spontaneous speech recordings. They test 12 instruction-tuned models (Llama-3, Mistral, Gemma variants, etc.) using a domain-informed prompt co-designed with clinical psychology and linguistics experts. Best models achieve Spearman correlations up to 0.8, and the authors analyze prediction variability and use word clouds to identify which linguistic features drive predictions.
Main takeaways:
- LLMs can extract semantically meaningful psychological cues from spontaneous speech in zero-shot mode (no training on this task).
- Tested on 111 participants from the PsyVoiD database, using only a few minutes of audio transcripts.
- Best models reach Spearman correlation of 0.8 on 80% of the data for predicting well-being scores.
- The prompt was designed with domain experts in clinical psychology and linguistics.
- Statistical and keyword analyses provide some explainability for what linguistic features the models latch onto.