65 citations · 117 across the 17 of their papers we have counts for
15 papers · 1 filter
When Does Reasoning Help in Machine Translation? A Hierarchical Analysis of LRM Reasoning Traces
Yuxiang Liu, Jiaming Luo, Eleftheria Briakou +1
Large Reasoning Models increasingly use intermediate traces for machine translation, but it remains unclear when such reasoning helps or hurts. We analyze reasoning traces across m…
Are Multilingual Models Actually Improving? Isolating True Cross-Lingual Transfer
Prasoon Bajpai, Eleftheria Briakou, Colin Cherry +2
Cross-lingual transfer is a model's ability to generalize capabilities from well-represented source languages to under-represented target languages. Existing measures of a model's…
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…
Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities
Gheorghe Comanici, Eric Bieber, Mike Schaekermann +3431
In this report, we introduce the Gemini 2.X model family: Gemini 2.5 Pro and Gemini 2.5 Flash, as well as our earlier Gemini 2.0 Flash and Flash-Lite models. Gemini 2.5 Pro is our…
SSA-COMET: Do LLMs Outperform Learned Metrics in Evaluating MT for Under-Resourced African Languages?
Senyu Li, Jiayi Wang, Felermino D. M. A. Ali +7
Evaluating machine translation (MT) quality for under-resourced African languages remains a significant challenge, as existing metrics often suffer from limited language coverage a…
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…
Leveraging Domain Knowledge at Inference Time for LLM Translation: Retrieval versus Generation
Bryan Li, Jiaming Luo, Eleftheria Briakou +1
While large language models (LLMs) have been increasingly adopted for machine translation (MT), their performance for specialist domains such as medicine and law remains an open ch…
SMOL: Professionally translated parallel data for 115 under-represented languages
Isaac Caswell, Elizabeth Nielsen, Jiaming Luo +23
We open-source SMOL (Set of Maximal Overall Leverage), a suite of training data to unlock machine translation for low-resource languages. SMOL has been translated into 124 (and gro…
Overestimation in LLM Evaluation: A Controlled Large-Scale Study on Data Contamination's Impact on Machine Translation
Muhammed Yusuf Kocyigit, Eleftheria Briakou, Daniel Deutsch +3
Data contamination -- the accidental consumption of evaluation examples within the pre-training data -- can undermine the validity of evaluation benchmarks. In this paper, we prese…
On the Implications of Verbose LLM Outputs: A Case Study in Translation Evaluation
Eleftheria Briakou, Zhongtao Liu, Colin Cherry +1
This paper investigates the impact of verbose LLM translations on evaluation. We first demonstrate the prevalence of this behavior across several LLM outputs drawn from the WMT 202…
Translating Step-by-Step: Decomposing the Translation Process for Improved Translation Quality of Long-Form Texts
Eleftheria Briakou, Jiaming Luo, Colin Cherry +1
In this paper we present a step-by-step approach to long-form text translation, drawing on established processes in translation studies. Instead of viewing machine translation as a…
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…
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…
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…
Quality-Aware Translation Models: Efficient Generation and Quality Estimation in a Single Model
Christian Tomani, David Vilar, Markus Freitag +5
Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability corre…