most citedTranslateGemma Technical Report

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cs.CL2026

Mind the Gap... or Not? How Translation Errors and Evaluation Details Skew Multilingual Results

Jan-Thorsten Peter, David Vilar, Tobias Domhan +2

Most current large language models (LLMs) support a wide variety of languages in addition to English, including high-resource languages (e.g. German, Chinese, French), as well as l…

cs.CL20262 cited

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…

cs.CL2025

Feeding Two Birds or Favoring One? Adequacy-Fluency Tradeoffs in Evaluation and Meta-Evaluation of Machine Translation

Behzad Shayegh, Jan-Thorsten Peter, David Vilar +4

We investigate the tradeoff between adequacy and fluency in machine translation. We show the severity of this tradeoff at the evaluation level and analyze where popular metrics fal…

cs.CL2025

You Cannot Feed Two Birds with One Score: the Accuracy-Naturalness Tradeoff in Translation

Gergely Flamich, David Vilar, Jan-Thorsten Peter +1

The goal of translation, be it by human or by machine, is, given some text in a source language, to produce text in a target language that simultaneously 1) preserves the meaning o…

cs.CL2025

Gemma 3 Technical Report

Gemma Team, Aishwarya Kamath, Johan Ferret +209

We introduce Gemma 3, a multimodal addition to the Gemma family of lightweight open models, ranging in scale from 1 to 27 billion parameters. This version introduces vision underst…