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20232026
most citedSeamlessM4T: Massively Multilingual & Multimodal Machine Translation

14 citations · 16 across the 4 of their papers we have counts for

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cs.CL2025

ZeroSumEval: An Extensible Framework For Scaling LLM Evaluation with Inter-Model Competition

Hisham A. Alyahya, Haidar Khan, Yazeed Alnumay +2

We introduce ZeroSumEval, a dynamic, competition-based, and evolving evaluation framework for Large Language Models (LLMs) that leverages competitive games. ZeroSumEval encompasses…

cs.CL20251 cited

Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study

Menglong Cui, Pengzhi Gao, Wei Liu +2

Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…

cs.CL2024

LLäMmlein: Transparent, Compact and Competitive German-Only Language Models from Scratch

Jan Pfister, Julia Wunderle, Andreas Hotho

We create two German-only decoder models, LLäMmlein 120M and 1B, transparently from scratch and publish them, along with the training data, for the German NLP research community to…

cs.CL2023

Seamless: Multilingual Expressive and Streaming Speech Translation

Seamless Communication, Loïc Barrault, Yu-An Chung +62

Large-scale automatic speech translation systems today lack key features that help machine-mediated communication feel seamless when compared to human-to-human dialogue. In this wo…

cs.CL202314 cited

SeamlessM4T: Massively Multilingual & Multimodal Machine Translation

Seamless Communication, Loïc Barrault, Yu-An Chung +65

What does it take to create the Babel Fish, a tool that can help individuals translate speech between any two languages? While recent breakthroughs in text-based models have pushed…