Research on the Application and Optimization Strategies of Deep Learning in Large Language Models

Abstract

The development of deep learning technology provides new opportunities for the construction and application of large language models. This paper systematically explores the current application status and optimization strategies of deep learning in large language models. The paper introduces the basic concepts and principles of deep learning and large language models, focusing on language representation methods, model architectures, and application cases. Addressing the challenges faced by large language models, the paper analyzes in detail optimization strategies such as model compression and acceleration, transfer learning and domain adaptation, data augmentation, and unsupervised learning. Through experiments on multiple benchmark datasets, the superior performance of deep learning models in tasks such as language understanding, text classification, named entity recognition, and question answering is confirmed, demonstrating their enormous potential in large language models. At the same time, the paper discusses the limitations of existing methods and proposes future research directions. This paper provides a comprehensive overview and insights into the application of deep learning in large language models, which is of great significance for advancing natural language processing technology.