Leandro Alfonso

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2024

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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
Proceedings of the 19th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2024)

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.
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