用于社交媒体上可解释心理压力检测的认知链
Cognition Chain for Explainable Psychological Stress Detection on Social Media
AI总结：
针对现有压力检测模型可解释性差且缺乏心理学理论指导的问题，提出基于认知评价理论的Cognition Chain方法，构建CogInstruct数据集微调Llama3得到CogLLM，在实现出色检测性能的同时增强了可解释性。
摘要：
压力是一个普遍的全球性健康问题，可能导致严重的心理健康问题。早期检测为压力相关疾病提供了及时的干预和预防手段。当前的早期检测模型执行“黑盒”推理，其可解释性和可信度有限，这阻碍了其在现实世界临床中的应用。得益于大型语言模型（LLMs）引入的生成特性，此类模型的决策和预测可通过相应的描述实现半可解释性。然而，现有的LLMs大多针对通用目的进行训练，缺乏心理学认知理论的指导。为此，我们首先通过观察为压力检测量身定制的思维链所带来的性能提升，突显了先验理论的重要性。这种被称为Cognition Chain的方法，基于认知评价理论，通过逐步的认知视角阐释了压力的产生过程，其进展流程为：刺激 $\ ightarrow$ 评估 $\ ightarrow$ 反应 $\ ightarrow$ 压力状态，从而引导LLMs提供全面的推理解释。我们进一步研究了所提出的Cognition Chain格式带来的益处，将其用作LLMs指令微调的合成数据集生成模板，并引入了CogInstruct，一个用于压力检测的指令微调数据集。该数据集采用三阶段自我反思标注流程开发，使LLMs能够自主生成并完善指令数据。通过使用CogInstruct对Llama3进行指令微调，我们开发了CogLLM，一个可解释的压力检测模型。评估结果表明，CogLLM在增强可解释性的同时取得了出色的性能。我们的工作通过将认知理论整合到LLM推理过程中，贡献了一种新颖的方法，为未来可解释AI研究提供了有前景的方向。
英文摘要：
Stress is a pervasive global health issue that can lead to severe mental health problems. Early detection offers timely intervention and prevention of stress-related disorders. The current early detection models perform "black box" inference suffering from limited explainability and trust which blocks the real-world clinical application. Thanks to the generative properties introduced by the Large Language Models (LLMs), the decision and the prediction from such models are semi-interpretable through the corresponding description. However, the existing LLMs are mostly trained for general purposes without the guidance of psychological cognitive theory. To this end, we first highlight the importance of prior theory with the observation of performance boosted by the chain-of-thoughts tailored for stress detection. This method termed Cognition Chain explicates the generation of stress through a step-by-step cognitive perspective based on cognitive appraisal theory with a progress pipeline: Stimulus $\rightarrow$ Evaluation $\rightarrow$ Reaction $\rightarrow$ Stress State, guiding LLMs to provide comprehensive reasoning explanations. We further study the benefits brought by the proposed Cognition Chain format by utilising it as a synthetic dataset generation template for LLMs instruction-tuning and introduce CogInstruct, an instruction-tuning dataset for stress detection. This dataset is developed using a three-stage self-reflective annotation pipeline that enables LLMs to autonomously generate and refine instructional data. By instruction-tuning Llama3 with CogInstruct, we develop CogLLM, an explainable stress detection model. Evaluations demonstrate that CogLLM achieves outstanding performance while enhancing explainability. Our work contributes a novel approach by integrating cognitive theories into LLM reasoning processes, offering a promising direction for future explainable AI research.
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