Self-Attention Explained: The Core of Large Language Models

This article explains the concept of self-attention, the key mechanism behind transformer models that power large language models (LLMs) like GPT and BERT. It discusses how self-attention enables LLMs to understand context and relationships across text sequences.

Details

Self-attention is the core mechanism that allows transformer models to process text effectively and power large language models. Unlike previous models like RNNs and LSTMs that processed text sequentially, self-attention enables direct connections between any two tokens in a sequence, regardless of distance. This allows the model to understand context and relationships across the entire input. Self-attention works by having each token compute a 'query' to determine what it is looking for, 'keys' to represent what other tokens offer, and 'values' to pass along relevant information. The model then compares each token's query to the keys of all other tokens, assigns attention weights based on relevance, and computes a weighted sum of the values. This results in a context-aware representation of each token. In practice, models use multiple attention heads to capture different linguistic patterns and relationships. Self-attention's parallelization, global context understanding, and flexible learning make it ideal for scaling large language models to handle complex language tasks like context understanding, reasoning, and in-context learning.