Attention is Naturally Sparse with Gaussian Distributed Input

• URL: http://arxiv.org/abs/2404.02690v1
• Date: Wed, 3 Apr 2024 12:37:34 GMT
• Title: Attention is Naturally Sparse with Gaussian Distributed Input
• Authors: Yichuan Deng, Zhao Song, Chiwun Yang,
• Abstract summary: This study presents a rigorous theoretical analysis of the sparsity in attention scores within Large Language Models (LLMs) Our main contribution lies in providing a detailed theoretical examination of how sparsity manifests in attention mechanisms, offering insights into the potential trade-offs between computational savings and model effectiveness.
• Score: 8.602260591839318
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The computational intensity of Large Language Models (LLMs) is a critical bottleneck, primarily due to the $O(n^2)$ complexity of the attention mechanism in transformer architectures. Addressing this, sparse attention emerges as a key innovation, aiming to reduce computational load while maintaining model performance. This study presents a rigorous theoretical analysis of the sparsity in attention scores within LLMs, particularly under the framework of Gaussian inputs. By establishing a set of foundational assumptions and employing a methodical theoretical approach, we unravel the intrinsic characteristics of attention score sparsity and its implications on computational efficiency. Our main contribution lies in providing a detailed theoretical examination of how sparsity manifests in attention mechanisms, offering insights into the potential trade-offs between computational savings and model effectiveness. This work not only advances our understanding of sparse attention but also provides a scaffold for future research in optimizing the computational frameworks of LLMs, paving the way for more scalable and efficient AI systems.

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