23 citations · 40 across the 6 of their papers we have counts for
6 papers
The Multilingual Divide and Its Impact on Global AI Safety
Aidan Peppin, Julia Kreutzer, Alice Schoenauer Sebag +13
Despite advances in large language model capabilities in recent years, a large gap remains in their capabilities and safety performance for many languages beyond a relatively small…
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…
Introducing v0.5 of the AI Safety Benchmark from MLCommons
Bertie Vidgen, Adarsh Agrawal, Ahmed M. Ahmed +97
This paper introduces v0.5 of the AI Safety Benchmark, which has been created by the MLCommons AI Safety Working Group. The AI Safety Benchmark has been designed to assess the safe…
A Keyword Based Approach to Understanding the Overpenalization of Marginalized Groups by English Marginal Abuse Models on Twitter
Kyra Yee, Alice Schoenauer Sebag, Olivia Redfield +3
Harmful content detection models tend to have higher false positive rates for content from marginalized groups. In the context of marginal abuse modeling on Twitter, such dispropor…
Multi-Domain Adversarial Learning
Alice Schoenauer-Sebag, Louise Heinrich, Marc Schoenauer +3
Multi-domain learning (MDL) aims at obtaining a model with minimal average risk across multiple domains. Our empirical motivation is automated microscopy data, where cultured cells…
Stochastic Gradient Descent: Going As Fast As Possible But Not Faster
Alice Schoenauer-Sebag, Marc Schoenauer, Michèle Sebag
When applied to training deep neural networks, stochastic gradient descent (SGD) often incurs steady progression phases, interrupted by catastrophic episodes in which loss and grad…