arXiv:2604.22128v2 Announce Type: replace 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
The proliferation of complex AI models necessitates a deeper understanding of their internal mechanisms for continued development and reliable deployment.
This research provides fundamental insights into how transformers process hierarchical information, a critical step towards building more robust and interpretable AI systems.
Our understanding of AI model interpretability and the causal roles of internal representations is advanced, moving beyond mere correlation to causation.
- · AI Researchers
- · AI Developers
- · Interpretability Tools Vendors
- · Companies Relying Solely on Black-Box AI
- · Less Interpretable AI Models
Improved debugging and optimization of transformer-based models for hierarchical tasks.
Development of new AI architectures explicitly designed for causal interpretability.
Accelerated deployment of AI in high-stakes domains requiring verifiable decision-making processes.
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