This research introduces a novel positional encoding technique that enables language models to effectively handle sequences longer than their training limit.

• Applies wavelet transforms to create position representations that naturally extrapolate to unseen lengths
• Overcomes the fundamental limitation of conventional position encodings that fail beyond trained sequence lengths
• Demonstrates improved performance on long-context tasks compared to existing methods
• Offers a practical engineering solution that can be integrated into various language model architectures

This advancement is particularly valuable for applications requiring processing of long documents, extended conversations, or any context-rich text that exceeds standard model context windows.

Wavelet-based Positional Representation for Long Context