You could have designed state of the art positional encoding
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This post explains the step-by-step discovery and iterative improvement of positional encoding in transformer models, culminating in Rotary Positional Encoding (RoPE) used in the latest LLaMA 3.2 release. It covers the necessity of positional information in self-attention mechanisms, desirable properties of an ideal encoding scheme, various intermediate approaches (including integer and binary positional encodings), and an in-depth analysis of sinusoidal and rotary encodings in the context of self-attention. The post also hints at future advancements in positional encoding.
Table of contents
Problem StatementMotivating ExampleDesirable PropertiesInteger Position EncodingBinary Position EncodingSinusoidal positional encodingAbsolute vs Relative Position EncodingPositional encoding in contextRo tary P ostional E ncodingExtending RoPE to n n n -DimensionsThe future of positional encodingConclusionReferencesSort: