This article is a systematic review of the text generation approaches to generative artificial intelligence (AI), discussing its methods, use cases, issues, and opportunities. One of the literature searches consisted of IEEE Xplore, SpringerLink, ScienceDirect, and Google Scholar using the following keywords: Generative AI, Transformer Models, Few-Shot Learning, Multimodal Learning, and Graph Neural Networks in natural language processing. The publications that were published since 2018 and addressed text generation tasks were considered. This paper identifies four important paradigms, namely, (i) CogView to generate text-to-image, (ii) few-shot instruction-tuned models to generate flexible text, (iii) entity-aware adversarial domain adaptation to detect robust named entities, or (iv) to classify multilingual text, a graph neural network is enhanced with language models. These methods are contrasted based on their scalability, accuracy, interpretability, and domain flexibility. Key issues, such as factual hallucination, bias, computational cost, and lack of explainability, are addressed in addition to ethical concerns. This review concludes that the developments to come will be based on hybrid architectures, retrieval-augmented generation, and energy-efficient training, accompanied by fairness and interpretability frameworks. This work presents an overview of modern scholarship in related fields, offering a comparison to scholars and practitioners designing future generations of generative AI systems to use in text generation.
Singh et al. (2026) studied this question.