用轻量级向量引导，提升大模型心理健康评估能力
Navigating through the hidden embedding space: steering LLMs to improve mental health assessment
- 通过线性变换特定层激活值，用引导向量控制模型输出
- 在两个任务上均提升效果：判断帖子相关性与补全心理问卷
- 无需复杂计算，适合资源有限场景下的模型优化
大型语言模型（LLMs）的快速发展正在推动人工智能在心理健康（MH）等敏感高影响领域的应用。然而，尽管有进展，小规模模型在领域特定任务中仍表现不佳。本文提出一种低成本但高效的方法，无需使用计算密集型技术即可提升LLM的心理健康评估能力。该方法通过对特定层激活值进行线性变换，并利用引导向量指导模型输出。实验表明，该干预使模型在两项任务中表现更优：（1）判断Reddit帖子是否有助于检测抑郁症状（相关性预测任务）；（2）根据用户Reddit发帖历史完成标准化抑郁心理筛查问卷（问卷补全任务）。结果凸显了引导机制作为计算高效工具，在LLM心理健康领域适配中的巨大潜力。
原文摘要 · Abstract (English)
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.
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