Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity
A large language model (LLM) can rate depression severity directly from a social media post, or mark which clinical criteria the post shows and let a simple rule turn the count into a label. The latter is easier to audit because a clinician can check each marked criterion. We compare these approaches on two Reddit corpora using three LLMs (from 9B to frontier scale) and two questionnaires (PHQ-9, BDI-II), and measure agreement with quadratic weighted kappa. For the two frontier models, criteria extraction scores above chain-of-thought on one corpus only when its decision thresholds are fitted on labeled data. Neither model's gain is significant, with or without recalibrating chain-of-thou...
| Authors | Xinkai Chen |
|---|---|
| Primary category | cs.CL |
| Published | 30 September 2026 |
| Last updated at source | 10 October 2026 |
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