28 citations · 57 across the 7 of their papers we have counts for
7 papers · 1 filter
TranslateGemma Technical Report
Mara Finkelstein, Isaac Caswell, Tobias Domhan +18
We present TranslateGemma, a suite of open machine translation models based on the Gemma 3 foundation models. To enhance the inherent multilingual capabilities of Gemma 3 for the t…
Don't Throw Away Data: Better Sequence Knowledge Distillation
Jun Wang, Eleftheria Briakou, Hamid Dadkhahi +3
A critical component in knowledge distillation is the means of coupling the teacher and student. The predominant sequence knowledge distillation method involves supervised learning…
When Scaling Meets LLM Finetuning: The Effect of Data, Model and Finetuning Method
Biao Zhang, Zhongtao Liu, Colin Cherry +1
While large language models (LLMs) often adopt finetuning to unlock their capabilities for downstream applications, our understanding on the inductive biases (especially the scalin…
To Diverge or Not to Diverge: A Morphosyntactic Perspective on Machine Translation vs Human Translation
Jiaming Luo, Colin Cherry, George Foster
We conduct a large-scale fine-grained comparative analysis of machine translations (MT) against human translations (HT) through the lens of morphosyntactic divergence. Across three…
Searching for Needles in a Haystack: On the Role of Incidental Bilingualism in PaLM's Translation Capability
Eleftheria Briakou, Colin Cherry, George Foster
Large, multilingual language models exhibit surprisingly good zero- or few-shot machine translation capabilities, despite having never seen the intentionally-included translation e…
The unreasonable effectiveness of few-shot learning for machine translation
Xavier Garcia, Yamini Bansal, Colin Cherry +5
We demonstrate the potential of few-shot translation systems, trained with unpaired language data, for both high and low-resource language pairs. We show that with only 5 examples…