Positional versus Symbolic Attention Heads: Learning Dynamics, RoPE Geometry, and Length Generalization
arXiv:2605.31558v1 Announce Type: new Abstract: Transformer-based language models are widespread in today's society. As such, understanding the mechanisms by which they solve structured tasks and predicting how they may behave in novel scenarios is of great importance for safe deployment. We study the learning dynamics of attention heads in a controlled setting by training a decoder-only Transformer (GPT-J) on two structurally equivalent multi-hop reasoning tasks: a number task requiring positional reasoning and a letter task requiring symbolic reasoning. Using a recently introduced metric tha
The proliferation of large language models necessitates a deeper understanding of their internal mechanisms for reliable and safe deployment.
Understanding how transformers reason will lead to more robust, predictable, and generalizable AI, crucial for advanced applications.
This research provides insights into the fundamental learning dynamics of transformer attention heads, guiding future model development and interpretability.
- · AI researchers
- · Transformer model developers
- · AI safety practitioners
- · Developers relying on black-box AI
- · Less interpretable AI approaches
Improved interpretability and debugging for transformer-based AI models.
Development of more efficient and task-specific attention mechanisms in future AI architectures.
Accelerated progress in building truly generalized AI systems capable of robust reasoning across diverse domains.
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