Structure vs. Chain-of-Thought: Evaluating LLM Criteria Extraction for Depression Severity

arXiv:2609.39049 · cs.CL, cs.AI · Submitted 2026-09-30 · Read on arXiv

cs.CL, cs.AI

Submitted: 2026-09-30

Updated: 2026-09-30

Code: https://github.com/xinkaichen97/depseverity-artifact

Terminology

• Calibration — 9× in this paper · explained in 7 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Commentary — 4× in this paper · explained in 2 episodes
• Benchmark — 3× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Chain-of-Thought Prompting — 2× in this paper · explained in 2 episodes
• Embeddings — 2× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Large Language Model (LLM) — 1× in this paper · explained in 3 episodes

Sources

• Data set creation and empirical analysis for detecting signs of depression from social media postings
• Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
• DepressionX: Knowledge Infused Residual Attention for Explainable Depression Severity Assessment

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