Large Pre Trained Model

Large pre-trained models (LPMs) are massive neural networks trained on enormous datasets, aiming to achieve strong generalization across diverse downstream tasks with minimal further training. Current research emphasizes efficient fine-tuning techniques, such as prompt engineering, low-rank adaptation (e.g., LoRA, SVFit), and sparse parameter updates, to reduce computational costs and improve model adaptability while addressing issues like overfitting and catastrophic forgetting. This field is significant due to LPMs' transformative impact on various applications, from natural language processing and computer vision to robotics and education, driving advancements in both theoretical understanding and practical deployment of AI systems.