Beyond Reproduction: Edit-Conditioned Adaptation for Unstructured Knowledge Editing

In unstructured knowledge editing, successfully reproducing a target document does not necessarily ensure that a model can use its individual facts to answer new queries. To narrow this reproduction–usability gap, we propose Edit-Conditioned Adaptation (ECoA), which trains lightweight adapters conditioned on the weight updates produced by an existing editor. ECoA freezes the edited base weights and trains low-rank adapters on continuation and cloze views constructed from the target document. It selects adapter layers using gradients of a document-derived training objective evaluated before and after editing, and modulates adapter rank-channel contributions using the responses of the editing updates to the current input activations. Evaluations across three language models and three unstructured knowledge editing benchmarks show consistent improvements in question answering over the corresponding base editors on UnKEBench and AKEW-CounterFact, with gains maintained in batch settings where multiple documents are edited into a single model. These results suggest that conditioning subsequent adaptation on existing editing updates provides an effective approach to improving query-conditioned use of edited knowledge.