Batch edits refer to the simultaneous modification of multiple specific pieces of knowledge, facts, or targeted behaviors within a trained machine learning model in a single update step. Instead of altering a model sequentially one correction at a time, batch editing applies optimization algorithms to incorporate large groups of updates concurrently into the model parameters or internal representations. This process is designed to improve computational efficiency and scalability when rectifying outdated or incorrect information, while aiming to preserve the model general capabilities and prevent collateral damage or performance degradation on unrelated knowledge.
