Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remained challenging due to contextual ambiguity, co-occurrence, and semantic overlap. We proposed a novel framework that combines Large Language Models (LLMs) with Multiple-Instance Learning (MIL) architecture to enhance interpretability and expression-level reasoning. Each utterance was decomposed into Emotion, Logic, and Behavior (ELB) components, which were processed by LLMs to infer multiple distortion instances, each with a predicted type, expression, and model-assigned salience score. These instances were integrated via a Multi-View Gated Attention mechanism for final classification. Experiments on Korean (KoACD) and English (Therapist QA) datasets demonstrate that incorporating ELB and LLM-inferred salience scores improves classification performance, especially for distortions with high interpretive ambiguity. Our results suggested a psychologically grounded and generalizable approach for fine-grained reasoning in mental health NLP.

翻译：认知扭曲与心理健康障碍密切相关，然而因其语境模糊性、共现性和语义重叠，自动检测仍面临挑战。我们提出了一种新颖框架，将大语言模型与多示例学习架构相结合，以增强可解释性和表达层面的推理能力。每个话语被分解为情绪、逻辑和行为三个组成部分，经由大语言模型处理以推断多个扭曲示例，每个示例包含预测类型、表达方式及模型分配的显著性分数。这些示例通过多视角门控注意力机制整合以进行最终分类。在韩语(KoACD)和英语(Therapist QA)数据集上的实验表明，融入情绪-逻辑-行为组件及大语言模型推断的显著性分数可提升分类性能，尤其对于高度解释歧义的扭曲类型效果显著。我们的研究结果为心理健康自然语言处理中的精细推理提供了一种具有心理学基础且可推广的方法。