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| Construction of a Web Break Information Knowledge Graph Based on Large Language Models Fine-tuned with Low-rank Adaptation |
| Received:September 24, 2025 Revised:November 14, 2025 |
| DOI：10.11980/j.issn.0254-508X.2026.04.024 |
| Key Words:large language models knowledge graph web break fault parameter-efficient fine-tuning |
| Author Name | Affiliation | Postcode | | ZENG Qingyu* | State Key Lab of Advanced Papermaking and Paper-based Materials, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | HONG Mengna* | State Key Lab of Advanced Papermaking and Paper-based Materials, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 | | LI Jigeng | State Key Lab of Advanced Papermaking and Paper-based Materials, South China University of Technology, Guangzhou, Guangdong Province, 510640 | 510640 |
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| Abstract:To achieve precise prediction and knowledge structuring of web break faults during the papermaking process, this study focused on the characteristics of web break data and the impact of papermaking equipment. By combining low-rank adaptation (LoRA) and chain-of-thought prompting engineering techniques, this research systematically compared the fine-tuning effects of four parameter-efficient fine-tuning (PEFT) strategies on large language models, including LoRA, weight-decomposed low-rank adaptation (DoRA), infused adapter by inhibiting and amplifying inner activations (IA3), and quantized low-rank adaptation (QLoRA). Based on the optimally LoRA fine-tuned large language models, ChatGLM3-6B, it constructed a web break information knowledge graph adapted to the entities, attributes, and relations within the papermaking domain. The results indicated that the LoRA algorithm achieved the best comprehensive performance in the entity recognition and relation extraction tasks for web break information, reaching a recall rate of 100% and an F1 score of 92.31%. Its performance significantly outperformed the non-fine-tuned ChatGLM3-6B and other mainstream large language models (such as iFLYTEK Spark MAX and Qwen2.5-7B). |
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