Position Encoding in Transformers: From Absolute and Relative Methods to Rotary Position Embeddings and Long-Context Scaling
A technical deep dive into the evolution of sequence order modeling from sinusoidal embeddings to RoPE scaling.
Self-attention models content-dependent interactions between tokens but does not by itself encode token order. Position encoding addresses this limitation by introducing absolute coordinates, relative distances, or position-dependent rotations into Transformer representations and...
ArXiv CS.CL
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Mastering position encoding is essential for LLMs to effectively navigate sequence order and maintain coherence over massive context windows. As researchers transition from fixed embeddings to dynamic scaling techniques, the ability to generalize beyond training lengths remains the primary bottleneck for future long-context models.
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