Understanding Attention Mechanisms, Self‑Attention, and Multi‑Head Attention in Transformers

Background: Attention mechanisms are inspired by human visual attention and have been used in computer vision since the 1980s, later extending to natural language processing.

Self‑Attention: In Transformer models, self‑attention replaces fixed‑size windows by computing pairwise relevance between all tokens, allowing parallel computation and handling of long sequences.

Scaled Dot‑Product Attention: The process multiplies input vectors by learned weight matrices Wq, Wk, and Wv to obtain queries (q), keys (k), and values (v), computes dot‑product scores α = q·k, applies softmax (or alternatives) to obtain attention weights, and aggregates values to produce output vectors.

Multi‑Head Attention: Multiple attention heads run the scaled dot‑product operation in parallel on different linear projections, then concatenate their results, enabling the model to capture diverse features such as syntactic and semantic information.

Positional Encoding: Since self‑attention lacks inherent order information, sinusoidal functions of position are added to token embeddings, allowing the model to distinguish token positions and generalize beyond the training sequence length.

Summary: Self‑attention and its extensions (multi‑head, positional encoding) are key to the efficiency and performance of large language models like GPT, and their parallel nature aligns well with GPU acceleration.

References: The concepts originate from the seminal paper “Attention Is All You Need” (arXiv:1706.03762) and further reading includes “Attention Mechanism in Neural Networks: Where it Comes and Where it Goes” (arXiv:2204.13154).