Sebastian Raschka explains positional embeddings in LLMs

This article explains how modern large language models encode and process token position information, covering classic methods like learned absolute embeddings and sinusoidal encodings alongside contemporary relative methods such as Rotary Position Embeddings (RoPE). The author walks through the underlying mechanisms with visual examples, mathematical formulas, and small PyTorch implementations, while also examining how modern architectures adapt or omit explicit positional encodings. Open article →