RETUYT-INCO at MLSP 2024: Experiments on Language Simplification using Embeddings, Classifiers and Large Language Models

Ignacio Sastre, Leandro Alfonso, Facundo Fleitas, Federico Gil, Andrés Lucas, Tomás Spoturno, Santiago Góngora, Aiala Rosá, Luis Chiruzzo

Abstract: In this paper we present the participation of the RETUYT-INCO team at the BEA-MLSP 2024 shared task. We followed different approaches, from Multilayer Perceptron models with word embeddings to Large Language Models fine-tuned on different datasets: already existing, crowd-annotated, and synthetic.Our best models are based on fine-tuning Mistral-7B, either with a manually annotated dataset or with synthetic data.

External IDs:dblp:conf/bea/SastreAFGLSGRC24