Research Paper on Transformers in Text Generation

This survey paper traces the evolution of text generation architectures from statistical models to modern neural approaches. We examine early methods including n-grams and Hidden Markov Models, analyzing their computational efficiency and limitations in capturing long-range dependencies. We then review Recurrent Neural Networks and Long Short-Term Memory networks, discussing their advances in sequential modeling alongside persistent challenges in parallelization and gradient stability. The core of this work focuses on the Transformer architecture and its derivatives. We analyze the self-attention mechanism, positional encoding, multi-head attention, and encoder-decoder structures that enabled models such as GPT, BERT, and T5. We examine how these architectures power contemporary applications including conversational agents, code generation tools, and machine translation systems. Finally, we critically evaluate current challenges: quadratic complexity in self-attention, limited context windows, training instability, hallucination, bias, and ethical concerns. We conclude by reviewing research directions in efficient architectures and responsible AI practices that will shape the future of natural language processing.