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This study investigates the causal role of specialized syntax attention heads in transformer models, finding that their influence on syntactic predictions is mixed and often negligible despite apparent specialization. The results suggest that model behavior may rely on distributed mechanisms rather than distinct, interpretable syntactic heads, offering critical insights for interpretability research.
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Do Syntax Heads Influence Model Behavior?

arXiv2 min read

arXiv:2610.19231v1 Announce Type: new Abstract: 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.

Title:Do Syntax Heads Influence Model Behavior?

Abstract: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.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2610.19231 [cs.CL]

(or arXiv:2610.19231v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.19231 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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Full text under the publisher's open licence · 203 words

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