arXiv subjects

Jihyun An

Publications and source records attributed to Jihyun An.

3 recordsLinked to original sources

arXiv2026

MentalBench: A DSM-Grounded Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models

Large language models (LLMs) have attracted growing interest as supportive tools for psychiatric assessment and clinical decision support. However, existing mental health benchmarks largely rely on social media data or supportive dialogue settings, limiting their ability to assess whether models can apply formal diagnostic criteria and differential diagnostic rules. In this paper, we introduce MentalBench, a benchmark for evaluating whether LLMs can make DSM-grounded psychiatric diagnostic decisions under varying levels of clinical ambiguity. At the core of MentalBench is MentalKG, a psychiatrist-built and validated knowledge graph encoding DSM-5 diagnostic criteria and differential diagnostic rules for 23 psychiatric disorders. Using MentalKG as an expert-curated logical backbone, we generate 24,750 synthetic clinical cases that systematically vary in information completeness and diagnostic complexity, enabling DSM-grounded evaluation. Our experiments show that although state-of-the-art LLMs perform well on noise-free queries that probe DSM-5 knowledge, they struggle to calibrate their confidence when distinguishing between disorders with overlapping symptoms. These findings raise concerns about the reliability of LLMs as psychiatric decision-support tools and highlight the need for more evaluation that reflects the diverse challenges in real-world psychiatric diagnosis.

cs.CL↗

arXiv2026

SynSym: A Synthetic Data Generation Framework for Psychiatric Symptom Identification

Psychiatric symptom identification on social media aims to infer fine-grained mental health symptoms from user-generated posts, allowing a detailed understanding of users' mental states. However, the construction of large-scale symptom-level datasets remains challenging due to the resource-intensive nature of expert labeling and the lack of standardized annotation guidelines, which in turn limits the generalizability of models to identify diverse symptom expressions from user-generated text. To address these issues, we propose SynSym, a synthetic data generation framework for constructing generalizable datasets for symptom identification. Leveraging large language models (LLMs), SynSym constructs high-quality training samples by (1) expanding each symptom into sub-concepts to enhance the diversity of generated expressions, (2) producing synthetic expressions that reflect psychiatric symptoms in diverse linguistic styles, and (3) composing realistic multi-symptom expressions, informed by clinical co-occurrence patterns. We validate SynSym on three benchmark datasets covering different styles of depressive symptom expression. Experimental results demonstrate that models trained solely on the synthetic data generated by SynSym perform comparably to those trained on real data, and benefit further from additional fine-tuning with real data. These findings underscore the potential of synthetic data as an alternative resource to real-world annotations in psychiatric symptom modeling, and SynSym serves as a practical framework for generating clinically relevant and realistic symptom expressions.

cs.CL↗

arXiv2026

Before and After ChatGPT: Revisiting AI-Based Dialogue Systems for Emotional Support

Mental health remains a major public health concern, while access to timely psychological support is often limited. AI-based dialogue systems have emerged as promising tools to address these barriers, and recent advances in large language models (LLMs) have significantly transformed this research area. However, a systematic understanding of this technological transition is still limited. This study reviews the technological evolution of AI-driven dialogue systems for mental health, focusing on the shift from task-specific deep learning models to LLM-based approaches. We conducted a bibliometric analysis and qualitative trend review of studies published between 2020 and May 2024 using Web of Science, Scopus, and the ACM Digital Library. The qualitative analysis compared research conducted before and after the widespread adoption of LLMs. Pre-LLM research was represented by highly cited studies and work based on the ESConv dataset, while post-LLM research included highly cited dialogue systems built on LLMs. A total of 146 studies met the inclusion criteria, showing a steady growth in publications over time. Before the widespread use of LLMs, empathetic response generation mainly relied on task-specific deep learning models. Highly cited and ESConv-based studies commonly focused on multi-task learning and the integration of external knowledge. In contrast, recent LLM-based dialogue systems demonstrate improved linguistic flexibility and generalization for emotional support. However, these systems also raise concerns related to reliability and safety in mental health applications. This review highlights the technological transition of AI-based dialogue systems for mental health in the LLM era. By identifying current research trends and limitations, the findings provide guidance for developing more effective and reliable AI-driven counseling systems.

cs.CY↗