Rotary Position Embedding

Rotary Position Embedding (RoPE) enhances language models by using relative positional encoding, which simplifies the attention mechanism and improves efficiency. It uses complex exponential functions to capture the relative distances between tokens, allowing for precise encoding of both local and global positional information. This method is crucial for managing large data sets and extending context lengths effectively.