Improving the Large Language Models (LMMs) Systems for Underrepresent Languages

ALEX MANUEL MARTINEZ AGUILERA

doi:10.36829/63CTS.v12i2.1785

Autores/as

ALEX MANUEL MARTINEZ AGUILERA Universidad Pedagógica Nacional Francisco Morazán https://orcid.org/0000-0002-9132-2608

DOI:

https://doi.org/10.36829/63CTS.v12i2.1785

Palabras clave:

Indigenous language documentation, generative AI, Reinforcement Learning from Human Feedback (RLHF), language revitalization

Resumen

Según Garcés, F. (2020), se hablan un total de 7000 lenguas en el mundo, de las cuales se estima que la mitad desaparecerán para finales del siglo XXI. Los motores de datos generativos de muchas empresas de inteligencia artificial cuentan con datos escasos sobre lenguas indígenas, ya que muchas de estas no tienen presencia en Internet (Kshetri, N., 2024). El presente estudio presenta un sistema basado en el conocimiento que proporciona una base de datos estable en lenguas indígenas, la cual puede ser utilizada posteriormente por el Aprendizaje a partir de Retroalimentación Humana (RLHF) para incorporar lenguas indígenas a los sistemas de inteligencia artificial.

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Citas

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