2025
XAutoLM: Efficient Fine-Tuning of Language Models via Meta-Learning and AutoML
Ernesto L. Estevanell-Valladares, Suilan Est{\'e}vez-Velarde, Yoan Guti{\'e}rrez, Andr{\'e}s Montoyo, Ruslan Mitkov
EMNLP 2025
Experts in machine learning leverage domain knowledge to navigate decisions in model selection, hyperparameter optimization, and resource allocation. This is particularly critical for fine-tuning language models (LMs), where repeated trials incur substantial computational overhead and environmental