On the Optimization and Generalization of Multi-head Attention
- URL: http://arxiv.org/abs/2310.12680v1
- Date: Thu, 19 Oct 2023 12:18:24 GMT
- Title: On the Optimization and Generalization of Multi-head Attention
- Authors: Puneesh Deora, Rouzbeh Ghaderi, Hossein Taheri, Christos Thrampoulidis
- Abstract summary: We investigate the potential optimization and generalization advantages of using multiple attention heads.
We derive convergence and generalization guarantees for gradient-descent training of a single-layer multi-head self-attention model.
- Score: 30.950284794162055
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The training and generalization dynamics of the Transformer's core mechanism,
namely the Attention mechanism, remain under-explored. Besides, existing
analyses primarily focus on single-head attention. Inspired by the demonstrated
benefits of overparameterization when training fully-connected networks, we
investigate the potential optimization and generalization advantages of using
multiple attention heads. Towards this goal, we derive convergence and
generalization guarantees for gradient-descent training of a single-layer
multi-head self-attention model, under a suitable realizability condition on
the data. We then establish primitive conditions on the initialization that
ensure realizability holds. Finally, we demonstrate that these conditions are
satisfied for a simple tokenized-mixture model. We expect the analysis can be
extended to various data-model and architecture variations.
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