Transformer language models develop attention heads that specialize in syntactic relations, for example, attending from verbs to their subjects.
Do these heads play a causal role in the model's syntactic predictions? Evidence for such a role is mixed to negative:
even when we force these heads to attend to a distractor noun other than the subject, the model's syntactic predictions are still more consistent with the subject than with the distractor.
Interventions on specialized syntax heads had a significantly stronger effect on syntactic predictions than interventions on random heads, but only in one of the two measures we propose.