116 citations · 136 across the 14 of their papers we have counts for
20 papers
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…
Building Multilingual Bridges: Data Mixing as the Pillar of Generalization for In-Language Reasoning
Mehrnaz Mofakhami, Ananya Sahu, Alejandro R. Salamanca +5
Reasoning language models have made substantial advances on a variety of complex tasks, yet their capabilities remain overwhelmingly English-centric: models primarily reason in Eng…
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…
Command A: An Enterprise-Ready Large Language Model
Team Cohere, :, Aakanksha +227
In this report we describe the development of Command A, a powerful large language model purpose-built to excel at real-world enterprise use cases. Command A is an agent-optimised…
Investigating the potential of Sparse Mixtures-of-Experts for multi-domain neural machine translation
Nadezhda Chirkova, Vassilina Nikoulina, Jean-Luc Meunier +1
We focus on multi-domain Neural Machine Translation, with the goal of developing efficient models which can handle data from various domains seen during training and are robust to…
Understanding and Mitigating Language Confusion in LLMs
Kelly Marchisio, Wei-Yin Ko, Alexandre Bérard +2
We investigate a surprising limitation of LLMs: their inability to consistently generate text in a user's desired language. We create the Language Confusion Benchmark (LCB) to eval…