  Mental health has attracted substantial attention in recent years and LLM can
be an effective technology for alleviating this problem owing to its capability
in text understanding and dialogue. However, existing research in this domain
often suffers from limitations, such as training on datasets lacking crucial
prior knowledge and evidence, and the absence of comprehensive evaluation
methods. In this paper, we propose a specialized psychological large language
model (LLM), named PsycoLLM, trained on a proposed high-quality psychological
dataset, including single-turn QA, multi-turn dialogues and knowledge-based QA.
Specifically, we construct multi-turn dialogues through a three-step pipeline
comprising generation, evidence judgment, and refinement. We augment this
process with real-world psychological case backgrounds extracted from online
platforms, enhancing the relevance and applicability of the generated data.
Additionally, to compare the performance of PsycoLLM with other LLMs, we
develop a comprehensive psychological benchmark based on authoritative
psychological counseling examinations in China, which includes assessments of
professional ethics, theoretical proficiency, and case analysis. The
experimental results on the benchmark illustrates the effectiveness of
PsycoLLM, which demonstrates superior performance compared to other LLMs.
