PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation
<|reference_start|>PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation: 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.<|reference_end|>
@article{hu2024psycollm:,
    title={PsycoLLM: Enhancing LLM for Psychological Understanding and Evaluation},
    author={Jinpeng Hu, Tengteng Dong, Luo Gang, Hui Ma, Peng Zou, Xiao Sun, Dan
  Guo, Meng Wang},
    journal={arXiv preprint arXiv:2407.05721},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.05721},
    primaryClass={cs.CL}
}