Generative Artificial Intelligence has grown exponentially as a result of Large Language Models (LLMs). This has been possible because of the impressive performance of deep learning methods created within the field of Natural Language Processing (NLP) and its subfield Natural Language Generation (NLG), which is the focus of this paper. Within the growing LLM family are the popular GPT-4, Bard and more specifically, tools such as ChatGPT have become a benchmark for other LLMs when solving most of the tasks involved in NLG research. This scenario poses new questions about the next steps for NLG and how the field can adapt and evolve to deal with new challenges in the era of LLMs. To address this, the present paper conducts a review of a representative sample of surveys recently published in NLG. By doing so, we aim to provide the scientific community with a research roadmap to identify which NLG aspects are still not suitably addressed by LLMs, as well as suggest future lines of research that should be addressed going forward.

翻译：生成式人工智能因大型语言模型（LLMs）的兴起而呈指数级增长。这一成就得益于自然语言处理（NLP）及其子领域自然语言生成（NLG）所创建的深度学习方法的卓越性能，而NLG正是本文关注的焦点。在日益壮大的LLM家族中，广为人知的GPT-4、Bard以及更具体的工具如ChatGPT，已成为解决NLG研究中大多数任务时其他LLM的基准参照。这一现状引发了关于NLG未来发展方向的新思考：该领域应如何调整与演进，以应对LLM时代的新挑战。为此，本文对近期发表的NLG领域代表性综述文献进行了系统梳理。通过这一工作，我们旨在为科学界提供一份研究路线图，以明确哪些NLG问题尚未被LLM充分解决，并指出未来需要着力探索的研究方向。