Search Results for author:
Found 9 papers, 1 papers with code
Training Neural Networks for Modularity aids Interpretability
An approach to improve network interpretability is via clusterability, i. e., splitting a model into disjoint clusters that can be studied independently.
Mimicry and the Emergence of Cooperative Communication
In this paper, we investigate a simple way in which the emergence of communication may be facilitated.
Learning Translations: Emergent Communication Pretraining for Cooperative Language Acquisition
In Emergent Communication (EC) agents learn to communicate with one another, but the protocols that they develop are specialised to their training community.
Improving Activation Steering in Language Models with Mean-Centring
Recent work in activation steering has demonstrated the potential to better control the outputs of Large Language Models (LLMs), but it involves finding steering vectors.
Joining the Conversation: Towards Language Acquisition for Ad Hoc Team Play
In this paper, we propose and consider the problem of cooperative language acquisition as a particular form of the ad hoc team play problem.
Low-Entropy Latent Variables Hurt Out-of-Distribution Performance
We study the relationship between the entropy of intermediate representations and a model's robustness to distributional shift.
A Measure of Explanatory Effectiveness
In most conversations about explanation and AI, the recipient of the explanation (the explainee) is suspiciously absent, despite the problem being ultimately communicative in nature.
Real-time Evolution of Multicellularity with Artificial Gene Regulation
This paper presents a real-time simulation involving ''protozoan-like'' cells that evolve by natural selection in a physical 2D ecosystem.
Learning to Communicate with Strangers via Channel Randomisation Methods
We introduce two methods for improving the performance of agents meeting for the first time to accomplish a communicative task.
