Domain Training

Domain training in machine learning focuses on adapting models trained on general data to perform effectively on specific, often data-scarce domains. Current research emphasizes leveraging large language models (LLMs) and techniques like few-shot learning, data augmentation, and instruction tuning to improve model performance in target domains, often using pre-trained models as a base and fine-tuning them with limited domain-specific data. This is crucial for applications where large, labeled datasets are unavailable, such as medical diagnosis, legal document analysis, and scientific knowledge graph construction. Improved domain adaptation techniques promise to enhance the applicability and reliability of machine learning across diverse fields.