Improving unified information extraction in Chinese mental health domain with instruction-tuned LLMs and type-verification component.

Journal: Artificial intelligence in medicine
PMID: 39987777

Abstract

BACKGROUND: Extracting psychological counseling help-seeker information from unstructured text is crucial for providing effective mental health support. This task involves identifying personal emotions, psychological states, and underlying psychological issues but faces significant challenges. These challenges include the sensitivity of mental health data, the lack of Chinese instruction datasets, and the difficulties large language models (LLMs) encounter with complex natural language understanding tasks.

Authors

  • Zijie Cai
    School of Computer Science and Mathematics, Fujian University of Technology, 350118, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, China. Electronic address: zhijie2961966@qq.com.
  • Hui Fang
    Department of Computer Science Loughborough University Loughborough UK.
  • Jianhua Liu
    School of Materials Science and Engineering, Ocean University of China, Qingdao 266100, China.
  • Ge Xu
    Fujian Provincial Key Laboratory of Information Processing and Intelligent Control (Minjiang University), China.
  • Yunfei Long
    School of Computer Science and Electronic Engineering, University of Essex, CO4 3SQ, Colchester, UK. Electronic address: yl20051@essex.ac.uk.
  • Yin Guan
    School of Computer and Big Data, Minjiang University, 350108, Fuzhou, China. Electronic address: niynaug@foxmail.com.
  • Tianci Ke
    School of Computer Science and Mathematics, Fujian University of Technology, 350118, Fuzhou, China; Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, 350118, China. Electronic address: tiancike@foxmail.com.

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