@inproceedings{stackhouse-debenedetto-2026-systematic,
title = "A Systematic Comparison of Parameter-Efficient Fine-Tuning Techniques for Low-Resource Neural Machine Translation: Evidence from Indigenous Languages of the {A}mericas",
author = "Stackhouse, Drew and
Debenedetto, Justin",
editor = "Mager, Manuel and
Ebrahimi, Abteen and
Bui, Minh Duc and
Pugh, Robert and
Oncevay, Arturo and
Chiruzzo, Luis and
Solano, Rolando Coto and
Rijhwani, Shruti and
Von Der Wense, Katharina",
booktitle = "Proceedings of the Sixth Workshop on {NLP} for Indigenous Languages of the {A}mericas ({A}mericas{NLP})",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.americasnlp-6.4/",
pages = "33--45",
ISBN = "979-8-89176-415-6",
abstract = "We present the first systematic benchmark of parameter-efficient fine-tuning (PEFT) for low-resource neural machine translation (NMT) of indigenous languages of the Americas. We evaluate eight PEFT methods alongside full fine-tuning on NLLB-200-distilled-600M across 13 indigenous-to-Spanish language pairs spanning four resource tiers (357-125,008 training sentences). OFT (Orthogonal Finetuning) achieves the highest development-set chrF++ among PEFT methods (26.63) while training only 0.28{\%} of parameters. LoRA (Low-Rank Adaptation) offers a strong efficiency-quality tradeoff (25.27 chrF++, 0.19{\%}). On held-out test data, full fine-tuning ranks first (25.12) with OFT a close second (25.06; p = 0.43). VeRA (Vector-based Random Matrix Adaptation) and Prefix Tuning consistently underperform. These results demonstrate that PEFT is a viable alternative to full fine-tuning for indigenous-language NMT."
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<abstract>We present the first systematic benchmark of parameter-efficient fine-tuning (PEFT) for low-resource neural machine translation (NMT) of indigenous languages of the Americas. We evaluate eight PEFT methods alongside full fine-tuning on NLLB-200-distilled-600M across 13 indigenous-to-Spanish language pairs spanning four resource tiers (357-125,008 training sentences). OFT (Orthogonal Finetuning) achieves the highest development-set chrF++ among PEFT methods (26.63) while training only 0.28% of parameters. LoRA (Low-Rank Adaptation) offers a strong efficiency-quality tradeoff (25.27 chrF++, 0.19%). On held-out test data, full fine-tuning ranks first (25.12) with OFT a close second (25.06; p = 0.43). VeRA (Vector-based Random Matrix Adaptation) and Prefix Tuning consistently underperform. These results demonstrate that PEFT is a viable alternative to full fine-tuning for indigenous-language NMT.</abstract>
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%0 Conference Proceedings
%T A Systematic Comparison of Parameter-Efficient Fine-Tuning Techniques for Low-Resource Neural Machine Translation: Evidence from Indigenous Languages of the Americas
%A Stackhouse, Drew
%A Debenedetto, Justin
%Y Mager, Manuel
%Y Ebrahimi, Abteen
%Y Bui, Minh Duc
%Y Pugh, Robert
%Y Oncevay, Arturo
%Y Chiruzzo, Luis
%Y Solano, Rolando Coto
%Y Rijhwani, Shruti
%Y Von Der Wense, Katharina
%S Proceedings of the Sixth Workshop on NLP for Indigenous Languages of the Americas (AmericasNLP)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-415-6
%F stackhouse-debenedetto-2026-systematic
%X We present the first systematic benchmark of parameter-efficient fine-tuning (PEFT) for low-resource neural machine translation (NMT) of indigenous languages of the Americas. We evaluate eight PEFT methods alongside full fine-tuning on NLLB-200-distilled-600M across 13 indigenous-to-Spanish language pairs spanning four resource tiers (357-125,008 training sentences). OFT (Orthogonal Finetuning) achieves the highest development-set chrF++ among PEFT methods (26.63) while training only 0.28% of parameters. LoRA (Low-Rank Adaptation) offers a strong efficiency-quality tradeoff (25.27 chrF++, 0.19%). On held-out test data, full fine-tuning ranks first (25.12) with OFT a close second (25.06; p = 0.43). VeRA (Vector-based Random Matrix Adaptation) and Prefix Tuning consistently underperform. These results demonstrate that PEFT is a viable alternative to full fine-tuning for indigenous-language NMT.
%U https://aclanthology.org/2026.americasnlp-6.4/
%P 33-45
Markdown (Informal)
[A Systematic Comparison of Parameter-Efficient Fine-Tuning Techniques for Low-Resource Neural Machine Translation: Evidence from Indigenous Languages of the Americas](https://aclanthology.org/2026.americasnlp-6.4/) (Stackhouse & Debenedetto, AmericasNLP 2026)
ACL