activity
20232026
most citedGemma 3 Technical Report

65 citations · 117 across the 17 of their papers we have counts for

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Showing 2025 · cs.CLShow all

6 papers · 2 filters

cs.CL2025★ 49 cited

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…

cs.CL2025

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…

cs.CL2025★ 65 cited

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…

cs.CL2025

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…

cs.CL2025

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…

cs.CL2025

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…