The Evolution and Breakthrough of Natural Language Processing: The Revolution from Rules to Deep Learning

Natural language processing (NLP) is the intersection of computer science and artificial intelligence. It aims to enable computers to understand and generate human natural language. With the development of the Internet and big data, natural language processing has become one of the most popular areas in the AI ​​era. Currently, the rise of large-scale pre-trained language models has greatly promoted progress in this field, making the application of natural language processing more extensive and in-depth. This article first reviews the development history of natural language processing, from early rule-based systems to current deep learning-based models. In particular, the proposal of the Transformer architecture marks a major breakthrough in natural language processing technology. It greatly improves the ability to handle long-distance dependencies through the attention mechanism, and has become the basic model for many NLP tasks. Further, this article explores the significant improvements in performance of large-scale pre-trained models such as GPT and BERT, and how they understand and generate language by learning the subtle laws of language on large amounts of text data. Finally, the perspective is returned to large language models, including the development history, performance and challenges of large models, and the introduction of meditation is proposed to solve the problem of model illusion.