20 citations · 23 across the 3 of their papers we have counts for
4 papers
ShiQ: Bringing back Bellman to LLMs
Pierre Clavier, Nathan Grinsztajn, Raphael Avalos +8
The fine-tuning of pre-trained large language models (LLMs) using reinforcement learning (RL) is generally formulated as direct policy optimization. This approach was naturally fav…
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…
Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning
Shivalika Singh, Freddie Vargus, Daniel Dsouza +30
Datasets are foundational to many breakthroughs in modern artificial intelligence. Many recent achievements in the space of natural language processing (NLP) can be attributed to t…
Sparsified Secure Aggregation for Privacy-Preserving Federated Learning
Irem Ergun, Hasin Us Sami, Basak Guler
Secure aggregation is a popular protocol in privacy-preserving federated learning, which allows model aggregation without revealing the individual models in the clear. On the other…