PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation

Understand

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 multi-turn QA generation, evidence judgment, and dialogue refinement.