Distance generalization in transformers: why bother with positional encoding?

TL;DR

This paper investigates distance generalization in transformers, focusing on performance when inter-token distances change between training and inference. The study examines the impact of positional encoding schemes and data diversity on distance resolution. It highlights the importance of understanding underlying mechanisms.

Why It Matters

Improving distance generalization can enhance transformer performance in real-world applications, such as text processing and language translation. Better understanding of the underlying mechanisms can inform the development of more effective models. This research has practical significance for natural language processing tasks.

Key Findings

• positional encoding schemes like RoPE and ALiBi improve distance resolution
• data diversity affects performance
• distance transfer learning can be positive or negative