1 citations · 1 across the 9 of their papers we have counts for
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North Small Translate: Advanced Cost-Effective Translation (Cohere CAT+)
Tom Kocmi, Alexandre Bérard, Phil Blunsom +20
We present North Small Translate, an open-weight, LLM-based machine translation (MT) model with instruction-following capabilities built on the same foundation as Cohere's Command…
Dynamically Allocating Evaluation Effort for Model Ranking
Vilém Zouhar, Julia Kreutzer, Alon Lavie +4
While human evaluation is the gold standard in many NLP tasks, it suffers from prohibitive costs and poor scalability. When identifying top-performing models, typical evaluation pr…
Contrastive ESA: Human Evaluation of Multiple Translations at Once
Vilém Zouhar, Roman Grundkiewicz, Sara Rajaee +6
Current human evaluation of machine translation typically assesses single outputs in isolation, a paradigm that suffers from high annotator noise and cost. We introduce Contrastive…
LKValues: Aligning Large Language Models with Sri Lankan Societal Values
Nethmi Muthugala, Supryadi, Surangika Ranathunga +7
Value alignment of Large Language Models (LLMs) has been shown to be culturally biased toward Western norms. This results in the mishandling of local values in multilingual societi…
Unlocking Reasoning Capability on Machine Translation in Large Language Models
Sara Rajaee, Sebastian Vincent, Alexandre Berard +3
Reasoning-oriented large language models (RLMs) achieve strong gains on tasks such as mathematics and coding by generating explicit intermediate reasoning. However, their impact on…
Pearmut: Human Evaluation of Translation Made Trivial
Vilém Zouhar, Tom Kocmi
Human evaluation is the gold standard for multilingual NLP, but is often skipped in practice and substituted with automatic metrics because it is notoriously complex and slow to se…