Evolution and Challenges of Natural Language Processing Technologies Based on Text Understanding and Generation

Abstract

Natural Language Processing (NLP) began in 1950, initially relying on rules established by linguists to parse text, but this approach proved insufficient for handling complex linguistic phenomena. With the development of technology, machine learning methods have been introduced to improve processing efficiency and accuracy through data-driven approaches. Today, neural networks and pre-trained models, such as BERT and GPT, have become mainstream. With their powerful data learning capabilities and deep semantic understanding, they have greatly expanded the application boundaries of natural language processing, showing unprecedented performance in everything from machine translation to text generation. This paper systematically reviews the development of natural language processing technology, analyzes the evolution from early rule-based methods to modern neural networks and pre-trained models (such as RNN, Transformer, BERT, GPT, etc.), and discusses the current status and problems of these technologies in text understanding, generation and cross-modal applications. The findings indicate that although natural language processing has shifted from “understanding” to “generation” and has gradually achieved “crossmodal intelligence,” problems such as model illusion, bias propagation, high energy consumption, poor interpretability, and data quality constraints remain current bottlenecks. Future research should focus on improving the reliability and security of models, optimizing resource utilization, designing interpretable artificial intelligence systems, and exploring knowledge enhancement and dynamic update mechanisms.