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

Understand

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.