多视角注意力多实例学习增强的认知扭曲检测
Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection
AI总结：
本文提出结合大语言模型与多实例学习架构的方法，通过分解情绪、逻辑和行为成分提升认知扭曲检测的可解释性和推理能力。
摘要：
认知扭曲与心理健康障碍密切相关，但其自动检测因语境模糊、共现和语义重叠而具有挑战性。我们提出一种结合大语言模型（LLM）与多实例学习（MIL）架构的新框架，以增强可解释性和表达层面的推理。每个陈述被分解为情绪、逻辑和行为（ELB）成分，通过LLM推断多个扭曲实例，每个实例具有预测类型、表达和模型分配的重要性分数。这些实例通过多视角门控注意力机制进行整合以实现最终分类。在韩语（KoACD）和英语（Therapist QA）数据集上的实验表明，结合ELB和LLM推断的重要性分数提高了分类性能，尤其是对于具有高可解释性模糊性的扭曲。我们的结果表明，这是一种基于心理基础且具有通用性的方法，用于心理健康NLP中的细粒度推理。数据集和实现细节已公开可访问。
英文摘要：
Cognitive distortions have been closely linked to mental health disorders, yet their automatic detection remains challenging due to contextual ambiguity, co-occurrence, and semantic overlap. We propose a novel framework that combines Large Language Models (LLMs) with a Multiple-Instance Learning (MIL) architecture to enhance interpretability and expression-level reasoning. Each utterance is decomposed into Emotion, Logic, and Behavior (ELB) components, which are processed by LLMs to infer multiple distortion instances, each with a predicted type, expression, and model-assigned salience score. These instances are 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 suggest a psychologically grounded and generalizable approach for fine-grained reasoning in mental health NLP. The dataset and implementation details are publicly accessible.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
