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This study investigates whether hierarchical representations in Transformer models are functionally utilized or merely passively accessible, using Dyck language tasks as a controlled benchmark. The findings reveal a critical divergence where attention mechanisms causally drive long-distance accuracy while residual stream signals remain decodable but less impactful when ablated. These insights clarify the internal mechanics of how neural networks encode and utilize structural information in sequence modeling.
Dissociating Decodability and Causal Use in Bracket-Sequence Transformers
arXiv:2604.22128v2 Announce Type: replace-cross Abstract: When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: in the geometry of the residual stream, and in stack-like attention patterns maintaining a last-in, first-out ordering. However, it remains unclear whether these representations are causally used or merely decodable. We examine this gap in transformers trained on the Dyck language (a formal language of balanced bracket sequences), where the hierarchical ground truth is explicit. By probing and intervening on the residual stream and attention patterns, we find that depth, distance, and top-of-stack signals are all decodable, yet their causal roles diverge. Specifically, masking attention to the true top-of-stack position causes a sharp drop in long-distance accuracy, while ablating low-dimensional residual stream subspaces has comparatively little effect. These results, which extend to a templated natural language setting, suggest that even in a controlled setting where the relevant hierarchical variables are known, decodability alone does not imply causal use.
Abstract:When trained on tasks requiring an understanding of hierarchical structure, transformers have been found to represent this hierarchy in distinct ways: in the geometry of the residual stream, and in stack-like attention patterns maintaining a last-in, first-out ordering. However, it remains unclear whether these representations are causally used or merely decodable. We examine this gap in transformers trained on the Dyck language (a formal language of balanced bracket sequences), where the hierarchical ground truth is explicit. By probing and intervening on the residual stream and attention patterns, we find that depth, distance, and top-of-stack signals are all decodable, yet their causal roles diverge. Specifically, masking attention to the true top-of-stack position causes a sharp drop in long-distance accuracy, while ablating low-dimensional residual stream subspaces has comparatively little effect. These results, which extend to a templated natural language setting, suggest that even in a controlled setting where the relevant hierarchical variables are known, decodability alone does not imply causal use.
Computation and Language (cs.CL); Machine Learning (cs.LG)
arXiv:2604.22128 [cs.CL]
(or arXiv:2604.22128v2 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2604.22128 Focus to learn more arXiv-issued DOI via DataCite
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