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About this paper
Xinyi Wu, Siyuan Liu, Ali Jadbabaie
A data-centered theory explains that Transformer models using Rotary Position Embeddings (RoPE) learn to utilize specific frequency bands as an adaptation to the relative-distance dependency structures present in their training data. This framework further establishes that Position Interpolation (PI) effectively extends context windows when data dependencies exhibit self-similarity across varying lengths.
Key insights
- RoPE frequency usage is not an intrinsic architectural property but a learned adaptation to the relative-distance dependency structures present in the training data.
- Models learn to select RoPE frequencies that match the width of task-relevant dependencies, driven by a field-resolution tradeoff that optimizes positional contrast.
- Position Interpolation (PI) effectively extends context by dilating the 'field' of RoPE frequencies and is successful for length generalization when test-time data dependencies are a self-similar, stretched version of training-time dependencies.
Summary by alphaXiv.
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