Contextscale
Contextscale is a term used in artificial intelligence and information processing to describe the extent to which contextual information influences a model’s predictions. It captures how much a given context—such as preceding text, user history, or surrounding modalities—alters the output compared with the same input without context. There is no single universal definition; researchers commonly quantify contextscale by comparing output distributions with and without context, or by measuring sensitivity to context tokens using gradients or ablation studies.
Contextscale can be shaped by model design and training. It is influenced by the size of the
In application, contextscale is relevant to long-form text generation, dialogue systems, document understanding, and recommender systems,
See also: context window, attention mechanism, memory networks, contextual embeddings.