1 citations · 3 across the 8 of their papers we have counts for
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Reverse Engineering Human Preferences with Reinforcement Learning
Lisa Alazraki, Tan Yi-Chern, Jon Ander Campos +3
The capabilities of Large Language Models (LLMs) are routinely evaluated by other LLMs trained to predict human preferences. This framework--known as LLM-as-a-judge--is highly scal…
Colombian Waitresses y Jueces canadienses: Gender and Country Biases in Occupation Recommendations from LLMs
Elisa Forcada Rodríguez, Olatz Perez-de-Viñaspre, Jon Ander Campos +2
One of the goals of fairness research in NLP is to measure and mitigate stereotypical biases that are propagated by NLP systems. However, such work tends to focus on single axes of…
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…
Multilingual Machine Translation with Open Large Language Models at Practical Scale: An Empirical Study
Menglong Cui, Pengzhi Gao, Wei Liu +2
Large language models (LLMs) have shown continuously improving multilingual capabilities, and even small-scale open-source models have demonstrated rapid performance enhancement. I…
From Tools to Teammates: Evaluating LLMs in Multi-Session Coding Interactions
Nathanaël Carraz Rakotonirina, Mohammed Hamdy, Jon Ander Campos +5
Large Language Models (LLMs) are increasingly used in working environments for a wide range of tasks, excelling at solving individual problems in isolation. However, are they also…
No Need for Explanations: LLMs can implicitly learn from mistakes in-context
Lisa Alazraki, Maximilian Mozes, Jon Ander Campos +3
Showing incorrect answers to Large Language Models (LLMs) is a popular strategy to improve their performance in reasoning-intensive tasks. It is widely assumed that, in order to be…