DocMEdit: Towards Document-Level Model Editing

Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost.Prior research has proposed a variety of datasets to assess the effectiveness of these model editing methods.However, most existing datasets only require models to output short phrases or sentences, overlooks the widespread existence of document-level tasks in the real world, raising doubts about their practical usability.Aimed at addressing this limitation and promoting the application of model editing in real-world scenarios, we propose the task of document-level model editing.To tackle such challenges and enhance model capabilities in practical settings, we introduce DocMEdit, a dataset focused on document-level model editing, characterized by document-level inputs and outputs, extrapolative, and multiple facts within a single edit.We propose a series of evaluation metrics and experiments.The results show that the difficulties in document-level model editing pose challenges for existing model editing methods 1 .