Context Window

The context window refers to the maximum number of tokens that a language model can attend to during a single forward pass. This limitation is critical in transformer architectures, where the self-attention mechanism computes representations based on the entire input sequence. The context window size directly impacts the model's ability to capture long-range dependencies and perform reasoning over extended documents. For instance, in models like GPT, the context window is fixed, typically ranging from a few hundred to several thousand tokens, depending on the architecture. This constraint necessitates careful design considerations in tasks requiring long-document processing, as exceeding the context window may lead to loss of relevant information. The context window is a fundamental aspect of model architecture that influences both the efficiency and effectiveness of language models in various applications.