Interpretable Symptom Vectors for Depression in a Large Language Model

arXiv:2609.01832 · cs.CL, cs.AI, cs.LG, q-bio.NC · Submitted 2026-09-01 · Read on arXiv

cs.CL, cs.AI, cs.LG, q-bio.NC

Submitted: 2026-09-01

Updated: 2026-09-01

Comments: 26 pages, 6 figures

Code: https://github.com/PrecisionNeuroLab/Symptom-Vectors-for-Depression

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Core — 31× in this paper · explained in 2 episodes
• Transformer — 9× in this paper · explained in 2 episodes
• AUC — 8× in this paper · explained in 2 episodes
• Interpretability — 6× in this paper · explained in 15 episodes
• Generalization — 5× in this paper · explained in 13 episodes
• Mechanistic interpretability — 5× in this paper · explained in 2 episodes
• Consistency — 4× in this paper · explained in 3 episodes
• Cosine Similarity — 4× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Steering — 2× in this paper · explained in 2 episodes
• Activation Addition — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Closed-Loop Control — 1× in this paper · explained in 3 episodes
• External Validation — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Hallucination — 1× in this paper · explained in 3 episodes
• Homogeneity — 1× in this paper · explained in 2 episodes
• Pairwise Comparisons — 1× in this paper · explained in 2 episodes
• Representation Engineering — 1× in this paper · explained in 2 episodes

Sources

• Evaluation of ChatGPT for NLP-based Mental Health Applications
• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Efficient Streaming Language Models with Attention Sinks
• Massive Activations in Large Language Models
• The Geometry of Categorical and Hierarchical Concepts in Large Language Models
• Representation Engineering: A Top-Down Approach to AI Transparency

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