Natural Language Processing in the Era of Large Language Models: A Critical Review

Abstract

Natural Language Processing (NLP) has undergone a transformative evolution with the emergence of Large Language Models (LLMs), which have significantly advanced the capabilities of artificial intelligence in understanding, generating, and reasoning with human language. This review critically examines the development of NLP from traditional statistical and machine learning approaches to the current era dominated by transformer-based architectures and foundation models. The paper explores the underlying principles of LLMs, including self-attention mechanisms, transfer learning, pre-training, and fine-tuning strategies, highlighting their contributions to state-of-the-art performance across a wide range of NLP tasks such as machine translation, text summarization, question answering, sentiment analysis, information extraction, and conversational AI. Furthermore, the review investigates the practical applications of LLMs in diverse domains including healthcare, education, finance, legal systems, software engineering, scientific research, and content generation.