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David Guzmán

2 accepted papers

2025

AlignFreeze: Navigating the Impact of Realignment on the Layers of Multilingual Models Across Diverse Languages

NAACL 2025short

Realignment techniques are often employed to enhance cross-lingual transfer in multilingual language models, still, they can sometimes degrade performance in languages that differ significantly from the fine-tuned source language. This paper introduces AlignFreeze, a method that freezes either the l…

Cited by 0SourcePDFScholar
2024

Unlocking Parameter-Efficient Fine-Tuning for Low-Resource Language Translation

NAACL 2024findings

Parameter-efficient fine-tuning (PEFT) methods are increasingly vital in adapting large-scale pre-trained language models for diverse tasks, offering a balance between adaptability and computational efficiency. They are important in Low-Resource Language (LRL) Neural Machine Translation (NMT) to enh…

Cited by 5SourcePDFScholar