How does rotary positional embedding improve generative model performance

Question

Can i know How does rotary positional embedding improve generative model performance?

Answer 1

You can use Rotary Positional Embedding (RoPE) to encode relative positions directly in attention scores, improving generalization to longer sequences.

Here is the code snippet below:

In the above code, we are using the following key points:

• Frequency-based angle generation for encoding relative positions

• Element-wise rotation of input embeddings using sine and cosine

• Applied only to a specific portion of the embedding (rotary dimension)

Hence, rotary embeddings boost generative model robustness on longer context inputs.