Position Embeddings

Position Embeddings are learnable or fixed vector representations that encode the sequential position of tokens within input sequences, enabling transformer models to understand word order and spatial relationships in text. This mechanism addresses the inherent limitation of self-attention architectures, which process tokens in parallel without inherent positional awareness. Position embeddings are added to input token embeddings before feeding data into transformer layers, providing models with crucial sequential information for tasks requiring positional understanding. The technique encompasses various approaches including absolute positional encodings, relative position representations, and rotary position embeddings that enhance long-range dependency modeling. Advanced implementations utilize sinusoidal functions, learned embeddings, and attention-based position encoding to optimize performance across different sequence lengths and tasks. Position embeddings enable transformers to maintain coherent understanding of sentence structure, temporal sequences, and spatial arrangements while preserving the computational efficiency of parallel processing architectures.