DocMEdit: Towards Document-Level Model Editing
Li Zeng, Zeming Liu, Chong Feng, Heyan Huang, Yuhang Guo

TL;DR
This paper introduces a new task and dataset for document-level model editing in large language models, addressing real-world needs by focusing on complex, multi-fact, document-based corrections.
Contribution
It presents enchmarkname, a novel dataset for document-level editing, along with evaluation metrics and experiments highlighting challenges for current methods.
Findings
Existing model editing methods struggle with document-level tasks.
The enchmarkname dataset enables assessment of complex, multi-fact edits.
Document-level editing presents unique challenges not captured by previous datasets.
Abstract
Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost.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 \benchmarkname, 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…
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Taxonomy
TopicsScientific Computing and Data Management · Semantic Web and Ontologies · Business Process Modeling and Analysis
