Interpretable Symptom Vectors for Depression in a Large Language Model
Quick summary
arXiv:2609.01832v1 Announce Type: cross Abstract: Patients with depression present with diverse symptom profiles, yet clinical practice routinely reduces this variation to a single severity score. Large language models (LLMs) can potentially capture various symptoms and their severity from patient speech. However, how depressive symptoms are represented inside LLMs remains poorly understood, limiting clinical trust. To examine whether internal model activations match clinician judgment, we analyzed the residual stream of Gemma-3-27B-PT using mechanistic interpretability techniques. Recording a
Key takeaways
- arXiv:2609.01832v1 Announce Type: cross Abstract: Patients with depression present with diverse symptom profiles, yet clinical practice routinely reduces this variation to a single severity score.
- Large language models (LLMs) can potentially capture various symptoms and their severity from patient speech.
- However, how depressive symptoms are represented inside LLMs remains poorly understood, limiting clinical trust.
Why it matters
“Interpretable Symptom Vectors for Depression in a Large Language Model” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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