Natural Language Processing: Technological Advancements, Ethical Challenges, And Sustainable Futures

Abstract

From basic rule-based methods to advanced neural networks supporting many uses across sectors, Natural Language Processing (NLP) has had an amazing evolution recently. With special focus on the paradigm changes induced by transformer-based models and the rise of large language models (LLMs), this review article critically analyses the direction of NLP progress. We follow important turning points in the development of the field: the neural network revolution, the change from symbolic systems to statistical methods, and the transforming power of self-attention techniques. Modern NLP has serious issues that demand multidisciplinary solutions even with great progress. These address low-resource language access, computing sustainability, model interpretability constraints, and natural biases in training data. Training large-scale models has grown to be a significant environmental problem stressing the need of energy-efficient architecture. Promising research fields found by our analysis to satisfy these challenges are human-AI cooperative frameworks, parameter-efficient fine-tuning methods, inventive approaches to few-shot and zero-shot learning, and multimodal integration strategies. Combining technical innovations with ethical concerns gives academics and professionals comprehensive knowledge of the present situation and future possibilities. We argue that the continuous growth of NLP demands not only technical innovation but also careful consideration of social impact, sustainability, and fair access among numerous language groups in terms of technical inventiveness.