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20222026
most citedMinistral 3

1 citations · 2 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CL2026

Shieldstral

Antonia Calvi, Avinash Sooriyarachchi, Giada Pistilli +273

We introduce Shieldstral, a 3B-parameter policy-adaptive multimodal safety classifier that matches or outperforms models nearly 7 its size on text safety benchmarks and set…

cs.CL2026★ 1 cited

Ministral 3

Alexander H. Liu, Kartik Khandelwal, Sandeep Subramanian +116

We introduce the Ministral 3 series, a family of parameter-efficient dense language models designed for compute and memory constrained applications, available in three model sizes:…

cs.CL2024★ 1 cited

Towards Zero-Shot Multimodal Machine Translation

Matthieu Futeral, Cordelia Schmid, Benoît Sagot +1

Current multimodal machine translation (MMT) systems rely on fully supervised data (i.e models are trained on sentences with their translations and accompanying images). However, t…

cs.CL2024

mOSCAR: A Large-scale Multilingual and Multimodal Document-level Corpus

Matthieu Futeral, Armel Zebaze, Pedro Ortiz Suarez +5

Multimodal Large Language Models (mLLMs) are trained on a large amount of text-image data. While most mLLMs are trained on caption-like data only, Alayrac et al. (2022) showed that…

cs.CL2022

Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation

Matthieu Futeral, Cordelia Schmid, Ivan Laptev +2

One of the major challenges of machine translation (MT) is ambiguity, which can in some cases be resolved by accompanying context such as images. However, recent work in multimodal…