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LLMs analyzed for depression symptom recognition and prediction · 3 sources tracked

By PulseAugur Editorial · [3 sources] ·

Researchers are exploring how large language models (LLMs) can be used to understand and predict depression symptom severity. One study analyzed the internal activations of Gemma-3-27B-PT, finding that specific layers and vectors correlate with clinician-defined symptom categories. Another approach uses a two-stage framework to generate and verify symptom candidates from sentences, improving accuracy over baseline methods. A third study identified interpretable lexical features in spoken language, such as reduced word count and pronoun usage, that are associated with depression severity across multilingual datasets. AI

IMPACT These studies explore novel methods for using LLMs to analyze and potentially diagnose mental health conditions, offering new avenues for clinical support and research.

RANK_REASON The cluster contains three academic papers detailing research into the application of LLMs and ML for depression symptom recognition and prediction.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

LLMs analyzed for depression symptom recognition and prediction · 3 sources tracked

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The cluster contains three academic papers detailing research into the application of LLMs and ML for depression symptom recognition and prediction.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Fangyi Zhu, Ajay Subramanian, Allison Constant, Camille Wang, Ravish Gupta, Corey J. Keller ·

    Interpretable Symptom Vectors for Depression in a Large Language Model

    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 sev…

  2. arXiv cs.CL TIER_1 English(EN) · Weiming Li, Catarina Barata, Miguel Constante, Joao Sanches ·

    Candidate Generation and Definition-Guided Verification for Sentence-Level Depression Symptom Recognition

    arXiv:2609.01833v1 Announce Type: new Abstract: Sentence-level recognition of depression symptoms is challenging because similar expressions can differ in symptom relevance, and language-model inference is insufficiently grounded in diagnostic definitions. This study proposes a t…

  3. arXiv cs.CL TIER_1 English(EN) · Anastasiia Tokareva, Judith Dineley, Zoe Firth, Pauline Conde, Faith Matcham, Sara Siddi, Femke Lamers, Ewan Carr, Carolin Oetzmann, Daniel Leightley, Yuezhou Zhang, Amos A. Folarin, Josep Maria Haro, Brenda W. J. H. Penninx, Raquel Bailon, Srinivasan Va… ·

    Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity

    arXiv:2511.07011v2 Announce Type: replace Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research to date has largely used non-clinical, cross-sectional written language and complex mach…

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