mlsanityData to 14 Oct 2026

Navigating through the hidden embedding space: steering LLMs to improve mental health assessment

arXiv 2510.16373 ↗First posted 18 Oct 2025cs.CL cs.AIcited by 0

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Abstract (arXiv)

The rapid evolution of Large Language Models (LLMs) is transforming AI, opening new opportunities in sensitive and high-impact areas such as Mental Health (MH). Yet, despite these advancements, recent evidence reveals that smaller-scale models still struggle to deliver optimal performance in domain-specific applications. In this study, we present a cost-efficient yet powerful approach to improve MH assessment capabilities of an LLM, without relying on any computationally intensive techniques. Our lightweight method consists of a linear transformation applied to a specific layer's activations, leveraging steering vectors to guide the model's output. Remarkably, this intervention enables the model to achieve improved results across two distinct tasks: (1) identifying whether a Reddit post is useful for detecting the presence or absence of depressive symptoms (relevance prediction task), and (2) completing a standardized psychological screening questionnaire for depression based on users' Reddit post history (questionnaire completion task). Results highlight the untapped potential of steering mechanisms as computationally efficient tools for LLMs' MH domain adaptation.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 35 references that are in the map. First posted on arXiv 18 Oct 2025.

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References · 9

  1. 2023Inference-Time Intervention: Eliciting Truthful Answers from a Language Modelcited by 1,302
  2. 2023Steering Llama 2 via Contrastive Activation Additioncited by 1,085
  3. 2024Refusal in Language Models Is Mediated by a Single Directioncited by 1,032
  4. 2023The Geometry of Truth: Emergent Linear Structure in Large Language Model Representations of True/False Datasetscited by 712
  5. 2024Emergence of a High-Dimensional Abstraction Phase in Language Transformerscited by 56
  6. 2025Understanding (Un)Reliability of Steering Vectors in Language Modelscited by 47
  7. 2021Early Risk Detection of Pathological Gambling, Self-Harm and Depression Using BERTcited by 16
  8. 2025Are LLMs effective psychological assessors? Leveraging adaptive RAG for interpretable mental health screening through psychometric practicecited by 11
  9. 2023DepreSym: A Depression Symptom Annotated Corpus and the Role of LLMs as Assessors of Psychological Markerscited by 5

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  1. 2023Mental-LLM: Leveraging Large Language Models for Mental Health Prediction via Online Text Data
  2. 2025A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG
  3. 2023Towards Interpretable Mental Health Analysis with Large Language Models
  4. 2026LLUMI: Improving LLM Writing Assistance for Mental Health Support with Online Community Feedback
  5. 2025Cognitive-Mental-LLM: Evaluating Reasoning in Large Language Models for Mental Health Prediction via Online Text
  6. 2024Emotion-Aware Embedding Fusion in LLMs (Flan-T5, LLAMA 2, DeepSeek-R1, and ChatGPT 4) for Intelligent Response Generation
  7. 2025Evaluating Large Language Models for Anxiety, Depression, and Stress Detection: Insights into Prompting Strategies and Synthetic Data
  8. 2023Rethinking Large Language Models in Mental Health Applications