Understanding Positional Embeddings in Modern LLMs

Modern large language models use various methods to encode token position information, with approaches ranging from classic learned absolute embeddings to contemporary techniques like RoPE (Rotary Position Embedding). This comprehensive guide covers the mechanisms behind these positional encoding methods, explains how they work in transformer architectures, and examines which techniques are used in current open-weight LLM models. The article serves as a technical foundation for understanding how different modern architectures handle positional information, from explicit encodings to those that omit them entirely.