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Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection

Jun Seo Kim, Hyemi Kim, Woo Joo Oh, Hongjin Cho, Hochul Lee, Hye Hyeon Kim

Abstract

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.

Multi-View Attention Multiple-Instance Learning Enhanced by LLM Reasoning for Cognitive Distortion Detection

Abstract

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.

Paper Structure

This paper contains 27 sections, 6 equations, 4 figures, 20 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Cognitive Distortions Detection
  4. Multiple-Instance Learning in NLP
  5. Dataset
  6. Emotion–Logic–Behavior Extraction
  7. LLM-Based Inference of Cognitive Distortion Instances
  8. Experiments
  9. MIL Setup and Bag Construction
  10. Embedding Representation
  11. MIL with Multi-View Gated Attention Mechanism
  12. Prediction and Evaluation
  13. Experimental Results and Analysis
  14. Effect of ELB on Label Coverage
  15. Input Configuration Comparison
  16. ...and 12 more sections

Figures (4)

  • Figure 1: LLM-based Inference of Cognitive Distortion Instances from ELB-Structured Utterances
  • Figure 2: ELB-Based Psychological Decomposition of an Utterance
  • Figure 3: Multi-View MIL Architecture for Cognitive Distortion Classification
  • Figure 4: Comparative Missing Rates of Cognitive Distortion Instances With and Without ELB (A: KoACD, B: Therapist QA)