Próximos Pasos en la Generación del Lenguaje Natural: Desafíos y Enfoques por Explorar

Generative Artificial Intelligence has experienced exponential growth largely due to the advent of Large Language Models (LLMs). This expansion is fueled by the impressive performance of deep learning methods used in Natural Language Processing (NLP) and its subfield, Natural Language Generation (NLG), which is the focus of this paper. Popular LLMs, such as GPT-4, Bard, and tools such as ChatGPT have set benchmarks for addressing various NLG tasks. This scenario raises critical questions regarding the future of NLG and its adaptation to emerging challenges in the LLM era. To explore these issues, the present paper reviews a representative sample of recent NLG surveys, thereby providing the scientific community with a research roadmap to identify NLG aspects that remain inadequately addressed and to suggest areas warranting further in-depth exploration in NLG.