Hochul Lee

Jun Seo Kim, Hyemi Kim, Woo Joo Oh et al.

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.

Albert Lee, Bonnie Lam, Wenyuan Li et al.

In this paper, we propose a new scheme for modelling the diverse behavior of neurons. We introduce the conditional activation, in which a neurons activation function is dynamically modified by a control signal. We apply this method to recreate behavior of special neurons existing in the human auditory and visual system. A heterogeneous multilayered perceptron (MLP) incorporating the developed models demonstrates simultaneous improvement in learning speed and performance across a various number of hidden units and layers, compared to a homogeneous network composed of the conventional neuron model. For similar performance, the proposed model lowers the memory for storing network parameters significantly.