This post is divided into five parts; they are: • Understanding Positional Encodings • Sinusoidal Positional Encodings • Learned Positional Encodings • Rotary Positional Encodings (RoPE) • Relative Positional Encodings Consider these two sentences: “The fox jumps over the dog” and “The dog jumps over the fox”.
This post is divided into three parts; they are: • Interpolation and Extrapolation in Sinusoidal Encodings and RoPE • Interpolation in Learned Encodings • YaRN for Larger Context Window Sinusoidal encodings excel at extrapolation due to their use of continuous functions: $$ begin{aligned} PE(p, 2i) &= sinleft(frac{p}{10000^{2i/d}}right) \ PE(p, 2i+1)…
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency similarity, and can be adaptive to the geometry of the underlying scene. We find that prior (absolute or relative) encoding…
We present a Positional Description Scheme (PDS) tailored for digit sequences, integrating placeholder value information for each digit. Given the structural limitations of subword tokenization algorithms, language models encounter critical Text Normalization (TN) challenges when handling numerical tasks. Our schema addresses this challenge through straightforward pre-processing, preserving the model architecture…