Innovative Applications of Large Models in Computer Science: Technological Breakthroughs and Future Prospects

As a research hotspot in the field of artificial intelligence in recent years, large models have broad application prospects in computer science and related fields. This paper discusses the theoretical basis, technical implementation, and application scenarios of large models. Firstly, the development process and basic principles of large models are systematically reviewed, and its key breakthroughs in computer fields such as natural language processing, computer vision, and speech recognition are analyzed. Secondly, the architecture design, training methods and optimization strategies of large models are emphatically expounded, and the innovations of large models in improving computing efficiency and reducing resource consumption are demonstrated. Thirdly, the typical cases of large models in computer applications such as intelligent question answering, automatic generation, and knowledge discovery are reviewed, and the challenges and limitations of large models in practical applications are analyzed. Finally, the future development trend of large models is prospected, and the key technical paths to further improve the performance of large models and expand the application boundaries are proposed. This paper focuses on the innovative application of large models in the field of computer science, and strives to provide valuable theoretical and practical guidance for relevant researchers and practitioners.