Rope to Nope and Back Again: A New Hybrid Attention Strategy

• URL: http://arxiv.org/abs/2501.18795v1
• Date: Thu, 30 Jan 2025 23:05:57 GMT
• Title: Rope to Nope and Back Again: A New Hybrid Attention Strategy
• Authors: Bowen Yang, Bharat Venkitesh, Dwarak Talupuru, Hangyu Lin, David Cairuz, Phil Blunsom, Acyr Locatelli,
• Abstract summary: Long-context large language models (LLMs) have achieved remarkable advancements, driven by techniques like Rotary Position Embedding (RoPE) This paper presents a comprehensive analysis of various attention mechanisms, including RoPE, No Positional Embedding (NoPE), and Query-Key Normalization (QK-Norm) We propose a novel architectural based on a hybrid attention mechanism that surpasses conventional RoPE-based transformer models in long context tasks and achieves competitive performance on benchmarks requiring shorter context lengths.
• Score: 18.13605820945755
• License:
• Abstract: Long-context large language models (LLMs) have achieved remarkable advancements, driven by techniques like Rotary Position Embedding (RoPE) (Su et al., 2023) and its extensions (Chen et al., 2023; Liu et al., 2024c; Peng et al., 2023). By adjusting RoPE parameters and incorporating training data with extended contexts, we can train performant models with considerably longer input sequences. However, existing RoPE-based methods exhibit performance limitations when applied to extended context lengths. This paper presents a comprehensive analysis of various attention mechanisms, including RoPE, No Positional Embedding (NoPE), and Query-Key Normalization (QK-Norm), identifying their strengths and shortcomings in long-context modeling. Our investigation identifies distinctive attention patterns in these methods and highlights their impact on long-context performance, providing valuable insights for architectural design. Building on these findings, we propose a novel architectural based on a hybrid attention mechanism that not only surpasses conventional RoPE-based transformer models in long context tasks but also achieves competitive performance on benchmarks requiring shorter context lengths.

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