Papers by Andrés Montoyo
XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML (2025.emnlp-main)
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Ernesto Luis Estevanell Valladares, Suilan Estevez-Velarde, Yoan Gutierrez, Andrés Montoyo, Ruslan Mitkov
| Challenge: | XAutoLM is a meta-learning-augmented framework that can be used to optimize discriminative and generative LM fine-tuning pipelines. |
| Approach: | They propose a meta-learning-augmented AutoML framework that reuses past experiences to optimize discriminative and generative LM fine-tuning pipelines efficiently. |
| Outcome: | XAutoLM surpasses zero-shot optimizer’s peak F1 on five of six tasks, reduces mean evaluation time of pipelines by up to 4.5x, and uncovers 50% more pipelines above zero- shot Pareto front. |
AutoML Strategy Based on Grammatical Evolution: A Case Study about Knowledge Discovery from Text (P19-1)
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| Challenge: | Recent advances in Automatic Machine Learning (AutoML) provide effective tools to explore large sets of algorithms, hyper-parameters and features to find out the best combination of them. |
| Approach: | They propose a novel AutoML strategy based on probabilistic grammatical evolution to explore the best combination of parameters and features to use when dealing with the knowledge discovery challenge in Spanish text documents. |
| Outcome: | The proposed strategy achieves state-of-the-art and provides interesting insights into the best combination of parameters and algorithms to use when dealing with this challenge. |