The Current Application Status and Prospects of Pruning Methods in natural language Processing

The rapid development of Natural Language Processing (NLP), driven by large-scale pre-trained models like BERT and GPT, has led to surging model parameters and computational complexity, resulting in high resource consumption and slow inference speed. Pruning, as an efficient model compression method, can significantly improve inference efficiency while maintaining model performance by removing redundant parameters or structures, and thus has important application value in NLP. This paper systematically reviews the current application status of pruning methods in NLP, including traditional methods such as weight pruning, structured pruning (such as layer pruning, attention head pruning), and analyzes the practical effects and limitations of these methods in tasks such as text classification, machine translation, question-answering systems, etc. The research shows that pruning techniques can effectively reduce the storage and computational overhead of large models, but still face challenges in dynamic pruning, sparsity optimization, and cross-task generalization. In the future, hybrid approaches that combine adaptive pruning, knowledge distillation, and hardware-aware pruning will become an important research direction. In addition, exploring the impact of pruning on model interpretability and robustness, as well as its fit for multimodal tasks, will also be a focus of future research. This paper aims to provide theoretical references and technical guidance for efficient model design and practice in the field of NLP.