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Roadmap for Natural Language Generation: Challenges and Insights

  • Autores: María Miró Maestre, Iván Martínez Murillo, Tania J. Martin, Borja Navarro Colorado Árbol académico, Antonio Ferrández Rodríguez Árbol académico, Armando Suárez Cueto Árbol académico, Elena Lloret Pastor Árbol académico
  • Localización: Procesamiento del lenguaje natural, ISSN 1135-5948, Nº. 74, 2025, págs. 67-79
  • Idioma: inglés
  • Títulos paralelos:
    • Próximos Pasos en la Generación del Lenguaje Natural: Desafíos y Enfoques por Explorar
  • Enlaces
  • Resumen
    • español

      La Inteligencia Artificial Generativa ha crecido exponencialmente debido, en gran medida, a la llegada de los Grandes Modelos del Lenguaje (LLMs). Esta expansión viene impulsada por el increíble rendimiento de los métodos de aprendizaje profundo utilizados en el Procesamiento del Lenguaje Natural (PLN) y su subcampo dedicado a la Generación del Lenguaje Natural (GLN), que supone el foco de este articulo. Algunos LLMs populares como GPT-4, Bard, y herramientas como ChatGPT se han convertido en referentes para abordar diversas tareas propias de la GLN. Este escenario plantea nuevas preguntas sobre el futuro de la GLN y su adaptación a los nuevos desafíos de la era de los LLMs. Para explorar estas cuestiones, el presente articulo analiza una muestra representativa de estudios recientes sobre la GLN, proporcionando así una hoja de ruta de investigación para identificar los aspectos de la GLN que siguen sin abordarse adecuadamente y proponer líneas de investigación que necesitan ser exploradas en profundidad para avanzar en la investigación en GLN.

    • English

      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.

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