RoPE's secret: it's not about decay, it's about frequency-based task sharing. 💡
And not all frequencies in RoPE are equal - some position tokens, others grasp semantics ✨
RoPE's frequency usage analysis reveals insights for enhancing LLM positional encodings.
High frequencies in RoPE construct positional attention, while low frequencies carry semantic information.
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📚 https://arxiv.org/abs/2410.06205
Original Problem 🔍:
One of the most popular types of encoding used today in LLMs are Rotary Positional Encodings (RoPE), that rotate the queries and keys based on their relative distance. A common belief is that RoPE is useful because it helps to decay token dependency as relative distance increases.
This paper argues that this is unlikely to be the core reason
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Solution in this Paper 🧠:
• Challenges RoPE's decay assumption with theoretical and empirical evidence
• Analyzes frequency usage in Gemma 7B model
• Proposes p-RoPE: removes lowest frequencies for robust semantic channels
• Mathematically proves RoPE's ability to construct positional attention patterns
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Key Insights from this Paper 💡:
• RoPE doesn't necessarily decay attention with distance
• High frequencies enable positional attention heads
• Low frequencies carry semantic information but lack long-context robustness
• Increasing RoPE wavelength improves long-context performance
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Results 📊:
• p-RoPE improves performance on 2B parameter models
• Wiki dataset: p-RoPE (0.75) achieves 4.4414 perplexity vs 4.4627 for RoPE
• FlanV2 dataset: p-RoPE (0.75) achieves 6.4422 perplexity vs 6.4429 for RoPE
• p-RoPE maintains performance even when removing 25% of frequencies
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