Yadi Zhao
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yadi Zhao has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bingqin Wang
Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing, China
1 shared publication - Di Xu
China Academy of Railway Sciences Co. Ltd
1 shared publication - Liangan Liu
Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Weitong Wu
Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China
1 shared publication - Xiaogang Li
College of Plant Protection Hunan Agricultural University Changsha China
1 shared publication - Xuequn Cheng
National Materials Corrosion and Protection Data Center, Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 21 August 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (16) , 9226
Domain-Specialized Large Language Model for Corrosion Analysis: Construction and Evaluation of Corr-Lora-RAG
This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enhance the model’s reproducibility and adaptability. Based on the Qwen2.5-7B model, the Corr-Lora model was developed by integrating prompt engineering and low-rank adaptation (LoRA) supervised fine-tuning (SFT) techniques, thereby improving the understanding and expression of domain-specific knowledge in the field of corrosion. Furthermore, the Corr-Lora-RAG model was built using retrieval-augmented generation (RAG) technology, enabling dynamic access to external knowledge. Experimental results demonstrate that the proposed model outperforms baseline models in terms of accuracy, completeness, and domain relevance, and exhibits knowledge generation capabilities comparable to those of large-scale language models under limited computational resources. This approach provides an intelligent solution for corrosion risk assessment, standards compliance analysis, and protective strategy formulation, and offers a valuable reference for the development of specialized language models in other engineering fields.
- 21 August 2025
- Published by MDPI AG in Applied Sciences
- Vol. 15 (16) , 9226
Open AccessDomain-Specialized Large Language Model for Corrosion Analysis: Construction and Evaluation of Corr-Lora-RAG
This study proposes a large language model, Corr-Lora-RAG, designed to address the complexity and uncertainty inherent in corrosion data. A dedicated corrosion knowledge database (CKD) was constructed, and dataset generation code was provided to enhance the model’s reproducibility and adaptability. Based on the Qwen2.5-7B model, the Corr-Lora model was developed by integrating prompt engineering and low-rank adaptation (LoRA) supervised fine-tuning (SFT) techniques, thereby improving the understanding and expression of domain-specific knowledge in the field of corrosion. Furthermore, the Corr-Lora-RAG model was built using retrieval-augmented generation (RAG) technology, enabling dynamic access to external knowledge. Experimental results demonstrate that the proposed model outperforms baseline models in terms of accuracy, completeness, and domain relevance, and exhibits knowledge generation capabilities comparable to those of large-scale language models under limited computational resources. This approach provides an intelligent solution for corrosion risk assessment, standards compliance analysis, and protective strategy formulation, and offers a valuable reference for the development of specialized language models in other engineering fields.