Copy Suppression: Comprehensively Understanding an Attention Head
Callum McDougall Arthur Conmy Cody Rushing Thomas McGrath Neel Nanda
We present a single attention head in GPT-2 Small that has one main role
across the entire training distribution. If components in earlier layers
predict a certain token, and this token appears earlier in the context, the
head suppresses it: we call this copy suppression. Attention Head 10.7 (L10H7)
suppresses naive copying behavior which improves overall model calibration.
This explains why multiple prior works studying certain narrow tasks found
negative heads that systematically favored the wrong answer. We uncover the
mechanism that the Negative Heads use for copy suppression with weights-based
evidence and are able to explain 76.9% of the impact of L10H7 in GPT-2 Small.
To the best of our knowledge, this is the most comprehensive description of the
complete role of a component in a language model to date. One major effect of
copy suppression is its role in self-repair. Self-repair refers to how ablating
crucial model components results in downstream neural network parts
compensating for this ablation. Copy suppression leads to self-repair: if an
initial overconfident copier is ablated, then there is nothing to suppress. We
show that self-repair is implemented by several mechanisms, one of which is
copy suppression, which explains 39% of the behavior in a narrow task.
Interactive visualisations of the copy suppression phenomena may be seen at our
web app https://copy-suppression.streamlit.app/