3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.CR2024★ 3 cited
Stealing User Prompts from Mixture of Experts
Itay Yona, Ilia Shumailov, Jamie Hayes +1
Mixture-of-Experts (MoE) models improve the efficiency and scalability of dense language models by routing each token to a small number of experts in each layer. In this paper, we…
cs.LG2024★ 1 cited
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou +6
Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract their data from machine learning models on request. Shortly after, inexact schemes were…
cs.CR2024
Buffer Overflow in Mixture of Experts
Jamie Hayes, Ilia Shumailov, Itay Yona
Mixture of Experts (MoE) has become a key ingredient for scaling large foundation models while keeping inference costs steady. We show that expert routing strategies that have cros…