Positional Encoding
Overview
Positional encoding supplies Transformers with information about where tokens occur, helping attention distinguish order, distance, and structure. Common approaches include learned or sinusoidal absolute embeddings, relative attention biases such as ALiBi, and rotary position embeddings (RoPE); newer methods encode geometry, time, or graph structure for images, videos, point clouds, and networks. Work also explores extending context length, reducing encoding and attention costs, and adapting positional signals to the input or task. A central tension is that methods that work well within training lengths may not generalize reliably to longer contexts, while gains in retrieval or spatial reasoning can come with trade-offs in other tasks.
Latest papers
115 papers18 papers in the last four weeks, up 64% on the four weeks before. 0.1% of all new papers.