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        <title>System Report for CCL25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional Chinese Medicine Diagnosis and Treatment</title>
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    <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.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional Chinese Medicine Diagnosis and Treatment
%A Zhang, Jian
%A Zhu, Wei
%A Tang, Zhiwen
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F zhang-etal-2025-system
%X “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.”
%U https://aclanthology.org/2025.ccl-2.44/
%P 369-375

Markdown (Informal)

[System Report for CCL25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional Chinese Medicine Diagnosis and Treatment](https://aclanthology.org/2025.ccl-2.44/) (Zhang et al., CCL 2025)

• System Report for CCL25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional Chinese Medicine Diagnosis and Treatment (Zhang et al., CCL 2025)

ACL

• Jian Zhang, Wei Zhu, and Zhiwen Tang. 2025. System Report for CCL25-Eval Task 9: Leveraging Chain-of-Thought and Multi-task Learning for Optimized Traditional Chinese Medicine Diagnosis and Treatment. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 369–375, Jinan, China. Chinese Information Processing Society of China.