Understanding Positional Embeddings in Modern LLMs

Why it matters

This article is a technical explainer on how large language models encode the position of tokens, a building block that determines whether a model can tell who is doing what in a sentence. The author contrasts a classic GPT-style architecture, which adds a position vector to each token embedding before the first transformer block, with a recently released model that instead applies a method called RoPE inside attention within each block. The piece grounds the discussion in a simple example where swapping two words changes a sentence's meaning, and notes that identical tokens receive identical embeddings unless position is encoded. It also observes that the causal mask used by autoregressive models provides some implicit order information. The article pairs conceptual explanation with visualizations, math, and a small PyTorch implementation, and is positioned as a foundation for a follow-up piece on current architecture trends.