Language Models (LMs) are being proposed for mental health applications where the heightened risk of adverse outcomes means predictive performance may not be a sufficient litmus test of a model's utility in clinical practice. A model that can be trusted for practice should have a correspondence between explanation and clinical determination, yet no prior research has examined the attention fidelity of these models and their effect on ground truth explanations. We introduce an evaluation design that focuses on the robustness and explainability of LMs in identifying Wellness Dimensions (WDs). We focus on two existing mental health and well-being datasets: (a) Multi-label Classification-based MultiWD, and (b) WellXplain for evaluating attention mechanism veracity against expert-labeled explanations. The labels are based on Halbert Dunn's theory of wellness, which gives grounding to our evaluation. We reveal four surprising results about LMs/LLMs: (1) Despite their human-like capabilities, GPT-3.5/4 lag behind RoBERTa, and MedAlpaca, a fine-tuned LLM on WellXplain fails to deliver any remarkable improvements in performance or explanations. (2) Re-examining LMs' predictions based on a confidence-oriented loss function reveals a significant performance drop. (3) Across all LMs/LLMs, the alignment between attention and explanations remains low, with LLMs scoring a dismal 0.0. (4) Most mental health-specific LMs/LLMs overlook domain-specific knowledge and undervalue explanations, causing these discrepancies. This study highlights the need for further research into their consistency and explanations in mental health and well-being.

翻译：语言模型正被提议应用于心理健康领域，其中不良后果风险的增加意味着预测性能可能不足以作为模型在临床实践中实用性的试金石。一个值得临床信赖的模型应具备解释与临床判断之间的对应关系，然而先前研究尚未检验这些模型的注意力保真度及其对真实解释的影响。本文提出一种专注于语言模型在识别健康维度任务中鲁棒性与可解释性的评估框架。我们聚焦于两个现有心理健康与福祉数据集：(a) 基于多标签分类的MultiWD数据集，(b) 用于通过专家标注解释评估注意力机制真实性的WellXplain数据集。数据标签基于Halbert Dunn的健康理论，这为我们的评估提供了理论基础。我们揭示了关于语言模型/大型语言模型的四个意外发现：(1) 尽管具备类人能力，GPT-3.5/4的性能落后于RoBERTa，而在WellXplain上微调的大型语言模型MedAlpaca未能带来性能或解释层面的显著改进。(2) 基于置信度导向损失函数重新检验语言模型的预测时，其性能出现显著下降。(3) 所有语言模型/大型语言模型的注意力与解释对齐度均处于低位，其中大型语言模型得分低至0.0。(4) 多数心理健康专用语言模型/大型语言模型忽视了领域特定知识并低估解释价值，从而导致这些差异。本研究强调需要进一步探究其在心理健康与福祉领域的预测一致性与解释机制。