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About this paper
Philipp Dufter, Martin Schmitt, Hinrich Schütze
This survey paper from LMU Munich provides a systematic overview of techniques for incorporating position information into Transformer models, categorizing over 30 distinct approaches. It unifies notation and identifies key properties and trade-offs of absolute, relative, and combined positional encodings, offering a structured understanding of this dynamic field.
Key insights
- Position information methods broadly fall into adding explicit position embeddings (APE) or modifying the attention matrix (MAM), with APE often implicitly influencing MAM.
- Absolute position encodings are simpler but may struggle with unseen sequence lengths, whereas relative encodings intuitively model local linguistic dependencies and long-range relationships.
- Many state-of-the-art models combine both absolute and relative position information, suggesting a complementary benefit for performance.
Summary by alphaXiv.
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