On the Optimization and Generalization of Multi-head Attention

• URL: http://arxiv.org/abs/2310.12680v2
• Date: Sat, 12 Oct 2024 04:12:31 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: 28.33164313549433
• License:
• 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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