Comparative and Performance Analysis of Different Deep Learning Models in Text Generation

Deep learning-based text generation plays a critical role in modern natural language processing. It supports applications such as automatic translation, storytelling, and content creation. Recent research has explored various neural network architectures for generating coherent and contextually relevant text. This paper systematically reviews and compares four major architectures: Recurrent Neural Networks, Variational Autoencoders, Generative Adversarial Networks, and Transformer-based models. The comparative analysis uses standard performance metrics and human evaluations to identify each models strengths and limitations. Results show that Transformer-based models outperform others in fluency, coherence, and context awareness, especially in tasks requiring long-range text understanding. However, controllability, interpretability, and ethical concerns still pose significant challenges. Future research directions include developing more efficient models, improving controllability mechanisms, and creating reliable evaluation methods. Overcoming these challenges will expand the practical use of text generation models in diverse real-world scenarios.