activity
20222024
most citedNo Language Left Behind: Scaling Human-Centered Machine Translation

373 citations · 383 across the 6 of their papers we have counts for

collaborators

6 papers

cs.CL20242 cited

Preliminary WMT24 Ranking of General MT Systems and LLMs

Tom Kocmi, Eleftherios Avramidis, Rachel Bawden +18

This is the preliminary ranking of WMT24 General MT systems based on automatic metrics. The official ranking will be a human evaluation, which is superior to the automatic ranking…

cs.CL20243 cited

The Language Barrier: Dissecting Safety Challenges of LLMs in Multilingual Contexts

Lingfeng Shen, Weiting Tan, Sihao Chen +6

As the influence of large language models (LLMs) spans across global communities, their safety challenges in multilingual settings become paramount for alignment research. This pap…

cs.CL20231 cited

Findings of the WMT 2023 Shared Task on Discourse-Level Literary Translation: A Fresh Orb in the Cosmos of LLMs

Longyue Wang, Zhaopeng Tu, Yan Gu +14

Translating literary works has perennially stood as an elusive dream in machine translation (MT), a journey steeped in intricate challenges. To foster progress in this domain, we h…

cs.CL2023

Narrowing the Gap between Zero- and Few-shot Machine Translation by Matching Styles

Weiting Tan, Haoran Xu, Lingfeng Shen +5

Large language models trained primarily in a monolingual setting have demonstrated their ability to generalize to machine translation using zero- and few-shot examples with in-cont…

cs.CL2022373 cited

No Language Left Behind: Scaling Human-Centered Machine Translation

NLLB Team, Marta R. Costa-jussà, James Cross +36

Driven by the goal of eradicating language barriers on a global scale, machine translation has solidified itself as a key focus of artificial intelligence research today. However,…

cs.CL20224 cited

Bitext Mining for Low-Resource Languages via Contrastive Learning

Weiting Tan, Philipp Koehn

Mining high-quality bitexts for low-resource languages is challenging. This paper shows that sentence representation of language models fine-tuned with multiple negatives ranking l…