1 citations · 1 across the 3 of their papers we have counts for
4 papers
Understanding Task Representations in Neural Networks via Bayesian Ablation
Andrew Nam, Declan Campbell, Thomas Griffiths +2
Neural networks are powerful tools for cognitive modeling due to their flexibility and emergent properties. However, interpreting their learned representations remains challenging…
Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers
Andrew Nam, Henry Conklin, Yukang Yang +3
We present causal head gating (CHG), a scalable method for interpreting the functional roles of attention heads in transformer models. CHG learns soft gates over heads and assigns…
Levels of Analysis for Large Language Models
Alexander Y. Ku, Declan Campbell, Xuechunzi Bai +10
Modern artificial intelligence systems, such as large language models, are increasingly powerful but also increasingly hard to understand. Recognizing this problem as analogous to…
Discrete, compositional, and symbolic representations through attractor dynamics
Andrew Nam, Eric Elmoznino, Nikolay Malkin +3
Symbolic systems are powerful frameworks for modeling cognitive processes as they encapsulate the rules and relationships fundamental to many aspects of human reasoning and behavio…