提出文档级模型编辑任务，提升大模型在真实场景下的纠错能力。
DocMEdit: Towards Document-Level Model Editing
- 构建文档级编辑数据集，支持长文本输入输出和多事实修改。
- 现有编辑方法在文档级任务中表现显著下降，验证任务难度。
- 适合关注大模型真实应用与知识更新的研究者使用。
模型编辑旨在以低成本修正大语言模型中的错误和过时知识。现有研究虽提出多种评估数据集，但大多仅需模型输出短语或句子，忽视了现实世界中广泛存在的文档级任务，限制了其实际可用性。为解决此问题并推动模型编辑在真实场景的应用，本文提出文档级模型编辑任务。为此，我们引入 enchmarkname 数据集，具有文档级输入输出、可外推性及单次编辑包含多个事实的特点。设计了一系列评估指标与实验。结果表明，文档级模型编辑对现有方法构成显著挑战。
原文摘要 · Abstract (English)
Model editing aims to correct errors and outdated knowledge in the Large language models (LLMs) with minimal cost.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 evaluation metrics and experiments. The results show that the difficulties in document-level model editing pose challenges for existing model editing methods.
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