Developing large language models for display industrial knowledge: Data augmentation, training techniques, and evaluation strategies

• DOI: 10.1002/jsid.2064
• Publication Date: 2025-03-31T07:36:01Z
• Authors: Bingqian Wang; Lixin Wang; Qingqing Sun; Yulan Hu; Yuyu Liu; Xingqun Jiang

Abstract Large Language Models (LLMs) can be applied to many fields in the display industry. However, general LLMs lack domain‐specific knowledge and specialized terminology understanding, which results inaccurate responses when industrial question‐answering(Q&A) scenarios. To address this issue, work introduces a framework of Model training effectively import Display Industry Knowledge. This is specifically designed enhance comprehension ability on from industry field by improving data governance, distillation techniques, augmentation strategies, continual pre‐training mechanisms. approach not only significantly improves model's performance Q&A applications within but also prevents catastrophic forgetting common knowledge. Experimental demonstrate effectiveness these techniques. We hope that helpful for customization other domains.