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arXiv · 2502.03029

On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation

Nghiem T. Diep·Huy Nguyen·Chau Nguyen·Minh Le·Duy M. H. Nguyen·Daniel Sonntag·Mathias Niepert·Nhat Ho
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Abstract

The LLaMA-Adapter has recently emerged as an efficient fine-tuning technique for LLaMA models, leveraging zero-initialized attention to stabilize training and enhance performance. However, despite its empirical success, the theoretical foundations of zero-initialized attention remain largely unexplored. In this paper, we provide a rigorous theoretical analysis, establishing a connection between zero-initialized attention and mixture-of-expert models. We prove that both linear and non-linear prompts, along with gating functions, can be optimally estimated, with non-linear prompts offering greater flexibility for future applications. Empirically, we validate our findings on the open LLM benchmarks, demonstrating that non-linear prompts outperform linear ones. Notably, even with limited training data, both prompt types consistently surpass vanilla attention, highlighting the robustness and adaptability of zero-initialized attention.

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Nghiem T. Diep, Huy Nguyen, Chau Nguyen, Minh Le, Duy M. H. Nguyen, Daniel Sonntag, Mathias Niepert, Nhat Ho. 2025-02-05. On Zero-Initialized Attention: Optimal Prompt and Gating Factor Estimation. https://arxiv.org/abs/2502.03029

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