5 papers
Think in English, Answer in Korean: Efficient Adaptation of Multilingual Tool-Using Agents
Utsav Garg, Sungjin Hong, Jason Jung +6
We present LuckyStar 111B, a 111B-parameter hybrid reasoning model developed through a collaboration between Cohere and LG CNS for Korean-English enterprise agents under practical…
LLM Prompt Duel Optimizer: Efficient Label-Free Prompt Optimization
Yuanchen Wu, Saurabh Verma, Justin Lee +6
Large language models (LLMs) are highly sensitive to prompts, but most automatic prompt optimization (APO) methods assume access to ground-truth references (e.g., labeled validatio…
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…
Command R7B Arabic: A Small, Enterprise Focused, Multilingual, and Culturally Aware Arabic LLM
Yazeed Alnumay, Alexandre Barbet, Anna Bialas +9
Building high-quality large language models (LLMs) for enterprise Arabic applications remains challenging due to the limited availability of digitized Arabic data. In this work, we…
The Importance of Prompt Tuning for Automated Neuron Explanations
Justin Lee, Tuomas Oikarinen, Arjun Chatha +3
Recent advances have greatly increased the capabilities of large language models (LLMs), but our understanding of the models and their safety has not progressed as fast. In this pa…