Enhancing NLP Models with GenAI

N. Madrueño Sierro, I. Martín de Diego, A. Fernández Isabel

Recent advances in generative artificial intelligence (GenAI) offer new opportunities for improving natural language processing models. Specifically, the text generation and in-context learning capabilities of large language models (LLMs) can enhance model generalization and robustness. Within this context, a new framework is presented to improve systems across the model lifecycle. It addresses stages ranging from data preparation and robustness evaluation to defense against problematic inputs. The proposed framework demonstrates how LLMs can be leveraged effectively during both training and inference time. Consequently, it contributes to the development of more accurate and reliable NLP models for real-world applications.

Palabras clave: Generative artificial intelligence, Large language model, Prompt engineering, In-context learning

GT SW II: Inteligencia Artificial y Aprendizaje Automático
5 de septiembre de 2026  10:00
Aula 21

M. E. Escorihuela Sahún

D. Moctezuma, C. Lancho Martín

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