Joonhwan Chang

Jae-hee So, Joonhwan Chang, Eunji Kim et al.

Background: Advancements in large language models (LLMs) have opened new possibilities in psychiatric interviews, an underexplored area where LLMs could be valuable. This study focuses on enhancing psychiatric interviews by analyzing counseling data from North Korean defectors who have experienced trauma and mental health issues. Objective: The study investigates whether LLMs can (1) identify parts of conversations that suggest psychiatric symptoms and recognize those symptoms, and (2) summarize stressors and symptoms based on interview transcripts. Methods: LLMs are tasked with (1) extracting stressors from transcripts, (2) identifying symptoms and their corresponding sections, and (3) generating interview summaries using the extracted data. The transcripts were labeled by mental health experts for training and evaluation. Results: In the zero-shot inference setting using GPT-4 Turbo, 73 out of 102 segments demonstrated a recall mid-token distance d < 20 in identifying symptom-related sections. For recognizing specific symptoms, fine-tuning outperformed zero-shot inference, achieving an accuracy, precision, recall, and F1-score of 0.82. For the generative summarization task, LLMs using symptom and stressor information scored highly on G-Eval metrics: coherence (4.66), consistency (4.73), fluency (2.16), and relevance (4.67). Retrieval-augmented generation showed no notable performance improvement. Conclusions: LLMs, with fine-tuning or appropriate prompting, demonstrated strong accuracy (over 0.8) for symptom delineation and achieved high coherence (4.6+) in summarization. This study highlights their potential to assist mental health practitioners in analyzing psychiatric interviews.

Chungpa Lee, Joonhwan Chang, Jy-yong Sohn

Contrastive learning has emerged as a prominent branch of self-supervised learning for several years. Especially, CLIP, which applies contrastive learning to large sets of captioned images, has garnered significant attention. Recently, SigLIP, a variant of CLIP, has been proposed, which uses the sigmoid loss instead of the standard InfoNCE loss. SigLIP achieves the performance comparable to CLIP in a more efficient manner by eliminating the need for a global view. However, theoretical understanding of using the sigmoid loss in contrastive learning is underexplored. In this paper, we provide a theoretical analysis of using the sigmoid loss in contrastive learning, in the perspective of the geometric structure of learned embeddings. First, we propose the double-Constant Embedding Model (CCEM), a framework for parameterizing various well-known embedding structures by a single variable. Interestingly, the proposed CCEM is proven to contain the optimal embedding with respect to the sigmoid loss. Second, we mathematically analyze the optimal embedding minimizing the sigmoid loss for contrastive learning. The optimal embedding ranges from simplex equiangular-tight-frame to antipodal structure, depending on the temperature parameter used in the sigmoid loss. Third, our experimental results on synthetic datasets coincide with the theoretical results on the optimal embedding structures.