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Content-Based Addressing for Long Context

Godavarti, Mahesh
arXiv (OAI Expanded) · Papers
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68t0768t50computation-and-languagei-2-6i-2-7machine-learning
Rotary position embedding (RoPE) uses each token's integer position to determine the rotation applied inside attention. This works well for local token order, but increasing context length creates a positional train-test mismatch: RoPE produces relative rotations at offsets not seen during training.
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