Editing large language models refers to the process of precisely altering, updating, or correcting specific factual knowledge, behaviors, or associations within a trained model without undertaking resource-intensive full retraining. Unlike standard fine-tuning approaches that can inadvertently cause catastrophic forgetting or degrade unrelated capabilities, model editing applies localized modifications directly to internal parameters or through supplementary memory mechanisms. Its primary objective is to efficiently rectify errors, update outdated information, or remove undesirable outputs on specific target concepts while strictly preserving the accuracy, coherence, and overall performance of the model across all unrelated inputs.
