9 citations · 15 across the 15 of their papers we have counts for
5 papers · 1 filter
The Propensity for Density in Feed-forward Models
Nandi Schoots, Alex Jackson, Ali Kholmovaia +2
Does the process of training a neural network to solve a task tend to use all of the available weights even when the task could be solved with fewer weights? To address this questi…
Hidden in Plain Text: Emergence & Mitigation of Steganographic Collusion in LLMs
Yohan Mathew, Ollie Matthews, Robert McCarthy +4
The rapid proliferation of frontier model agents promises significant societal advances but also raises concerns about systemic risks arising from unsafe interactions. Collusion to…
Training Neural Networks for Modularity aids Interpretability
Satvik Golechha, Dylan Cope, Nandi Schoots
An approach to improve network interpretability is via clusterability, i.e., splitting a model into disjoint clusters that can be studied independently. We find pretrained models t…
Extending Activation Steering to Broad Skills and Multiple Behaviours
Teun van der Weij, Massimo Poesio, Nandi Schoots
Current large language models have dangerous capabilities, which are likely to become more problematic in the future. Activation steering techniques can be used to reduce risks fro…
Dissecting Language Models: Machine Unlearning via Selective Pruning
Nicholas Pochinkov, Nandi Schoots
Understanding and shaping the behaviour of Large Language Models (LLMs) is increasingly important as applications become more powerful and more frequently adopted. This paper intro…