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About this paper
M. Sagitova, O. Duranthon, L. Zdeborová
Researchers from EPFL developed a high-dimensional theoretical model for multi-head softmax attention, elucidating a two-stage learning dynamic for head specialization and revealing that standard softmax is suboptimal for the task. They introduced "Bayes-softmax" attention, which achieves optimal prediction performance and provides guidelines for head normalization and count.
Key insights
- Attention head training exhibits a two-stage dynamic: an initial fast, unspecialized phase followed by a slower, sequential specialization where heads align with latent signal structures.
- The transition to specialization is a sharp dynamical phase transition, with the specialization time becoming deterministic in the high-dimensional limit.
- Standard softmax attention is suboptimal in this setting due to persistent variance from unaligned heads, whereas "Bayes-softmax" can achieve Bayes-optimal prediction performance.
Summary by alphaXiv.
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