Advances in High-Performance Models for Natural Language Processing: A Review

This in-depth review looks at the most recent developments in high-performance models for Natural Language Processing (NLP), with a focus on transformer-based architectures and large language models (LLMs), which have changed the field. The rapid growth of model capabilities has changed the way machines understand, generate, and use human language, opening up new possibilities and problems in many areas. The review talks about important research trends, such as new ways to build transformer models, rules for scaling up performance, ways to make systems more efficient, how to make them work in more than one language, how to test them, how to think about ethics, how to protect them from attacks, how to explain them, and how to distill knowledge. Even though there has been a lot of progress, there are still big problems, such as needing a lot of computing power, ethical issues with bias and safety, not being able to understand things easily, and having trouble evaluating things. The review provides publications a structured overview of the current state of affairs, pointing out promising research directions and practical issues to think about when using high-performance NLP models. The results show how these technologies could change the world, but they also stress the need for responsible development that takes into account technical limitations and social effects.