@inproceedings{zhang-jian-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional {C}hinese Medicine Diagnosis and Treatment",
    author = "张坚, 张坚  and
      Zhu, Wei  and
      Tang, Zhiwen",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.44/",
    pages = "369--375",
    abstract = "``This paper introduces an intelligent diagnostic system for Traditional Chinese Medicine (TCM) that emulates clinical reasoning through a phased multi-turn dialogue process. The system architecture is divided into three sequential stages: syndrome differentiation, disease diagnosis,and prescription generation. Each stage leverages Chain-of-Thought (CoT) techniques to ensure coherent reasoning, maintaining contextual continuity and consistency throughout the diagnostic process. To optimize model performance, we employ a multi-task fine-tuning approach, combin-ing data from all three stages for training the Qwen2.5-7B-Instruct model. Experimental results show that the system achieves strong performance across all diagnostic tasks. Error analysis re-veals that the accuracy of the first two stages, syndrome differentiation and disease diagnosis, has a significant impact on the quality of the generated prescriptions. This work provides a scalable framework for intelligent TCM diagnosis, advancing both medical knowledge reasoning and the application of domain-specific large language models.''"
}