CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions

Abstract Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and...

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Main Authors: Can Liu, Kaijie Sun, Qingqing Zhou, Yuchen Duan, Jianhua Shu, Hongxing Kan, Zongyun Gu, Jili Hu
Format: Article
Language:English
Published: Nature Portfolio 2024-03-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-024-56874-w
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author Can Liu
Kaijie Sun
Qingqing Zhou
Yuchen Duan
Jianhua Shu
Hongxing Kan
Zongyun Gu
Jili Hu
author_facet Can Liu
Kaijie Sun
Qingqing Zhou
Yuchen Duan
Jianhua Shu
Hongxing Kan
Zongyun Gu
Jili Hu
author_sort Can Liu
collection DOAJ
description Abstract Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and effectively. In this study, we apply a pre-trained language model to the domain of CPM. We have meticulously assembled, processed, and released the first CPMI dataset and fine-tuned the ChatGLM-6B base model, resulting in the development of CPMI-ChatGLM. We employed consumer-grade graphics cards for parameter-efficient fine-tuning and investigated the impact of LoRA and P-Tuning v2, as well as different data scales and instruction data settings on model performance. We evaluated CPMI-ChatGLM using BLEU, ROUGE, and BARTScore metrics. Our model achieved scores of 0.7641, 0.8188, 0.7738, 0.8107, and − 2.4786 on the BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L and BARTScore metrics, respectively. In comparison experiments and human evaluation with four large language models of similar parameter scales, CPMI-ChatGLM demonstrated state-of-the-art performance. CPMI-ChatGLM demonstrates commendable proficiency in CPM recommendations, making it a promising tool for auxiliary diagnosis and treatment. Furthermore, the various attributes in the CPMI dataset can be used for data mining and analysis, providing practical application value and research significance.
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spelling doaj.art-1b03f88871d74452ac3c0b7bc62feac32024-03-17T12:25:14ZengNature PortfolioScientific Reports2045-23222024-03-0114111310.1038/s41598-024-56874-wCPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructionsCan Liu0Kaijie Sun1Qingqing Zhou2Yuchen Duan3Jianhua Shu4Hongxing Kan5Zongyun Gu6Jili Hu7School of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineSchool of Medical Informatics Engineering, Anhui University of Traditional Chinese MedicineAbstract Chinese patent medicine (CPM) is a typical type of traditional Chinese medicine (TCM) preparation that uses Chinese herbs as raw materials and is an important means of treating diseases in TCM. Chinese patent medicine instructions (CPMI) serve as a guide for patients to use drugs safely and effectively. In this study, we apply a pre-trained language model to the domain of CPM. We have meticulously assembled, processed, and released the first CPMI dataset and fine-tuned the ChatGLM-6B base model, resulting in the development of CPMI-ChatGLM. We employed consumer-grade graphics cards for parameter-efficient fine-tuning and investigated the impact of LoRA and P-Tuning v2, as well as different data scales and instruction data settings on model performance. We evaluated CPMI-ChatGLM using BLEU, ROUGE, and BARTScore metrics. Our model achieved scores of 0.7641, 0.8188, 0.7738, 0.8107, and − 2.4786 on the BLEU-4, ROUGE-1, ROUGE-2, ROUGE-L and BARTScore metrics, respectively. In comparison experiments and human evaluation with four large language models of similar parameter scales, CPMI-ChatGLM demonstrated state-of-the-art performance. CPMI-ChatGLM demonstrates commendable proficiency in CPM recommendations, making it a promising tool for auxiliary diagnosis and treatment. Furthermore, the various attributes in the CPMI dataset can be used for data mining and analysis, providing practical application value and research significance.https://doi.org/10.1038/s41598-024-56874-w
spellingShingle Can Liu
Kaijie Sun
Qingqing Zhou
Yuchen Duan
Jianhua Shu
Hongxing Kan
Zongyun Gu
Jili Hu
CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
Scientific Reports
title CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
title_full CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
title_fullStr CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
title_full_unstemmed CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
title_short CPMI-ChatGLM: parameter-efficient fine-tuning ChatGLM with Chinese patent medicine instructions
title_sort cpmi chatglm parameter efficient fine tuning chatglm with chinese patent medicine instructions
url https://doi.org/10.1038/s41598-024-56874-w
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