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

• URL: http://arxiv.org/abs/2602.12871v1
• Date: Fri, 13 Feb 2026 12:21:33 GMT
• Title: MentalBench: A Benchmark for Evaluating Psychiatric Diagnostic Capability of Large Language Models
• Authors: Hoyun Song, Migyeong Kang, Jisu Shin, Jihyun Kim, Chanbi Park, Hangyeol Yoo, Jihyun An, Alice Oh, Jinyoung Han, KyungTae Lim,
• Abstract summary: MentalBench is a benchmark for evaluating psychiatric diagnostic decision-making in large language models (LLMs)<n>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.
• Score: 28.184599359142307
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We introduce MentalBench, a benchmark for evaluating psychiatric diagnostic decision-making in large language models (LLMs). Existing mental health benchmarks largely rely on social media data, limiting their ability to assess DSM-grounded diagnostic judgments. 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 a golden-standard logical backbone, we generate 24,750 synthetic clinical cases that systematically vary in information completeness and diagnostic complexity, enabling low-noise and interpretable evaluation. Our experiments show that while state-of-the-art LLMs perform well on structured queries probing DSM-5 knowledge, they struggle to calibrate confidence in diagnostic decision-making when distinguishing between clinically overlapping disorders. These findings reveal evaluation gaps not captured by existing benchmarks.

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