Federico Gil
2024
RETUYT-INCO at MLSP 2024: Experiments on Language Simplification using Embeddings, Classifiers and Large Language Models
Ignacio Sastre
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Leandro Alfonso
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Facundo Fleitas
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Federico Gil
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Andrés Lucas
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Tomás Spoturno
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Santiago Góngora
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Aiala Rosá
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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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Co-authors
- Ignacio Sastre 1
- Leandro Alfonso 1
- Facundo Fleitas 1
- Andrés Lucas 1
- Tomás Spoturno 1
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- bea1