让大模型编辑知识时保持上下文一致，避免信息矛盾。
K-Edit: Language Model Editing with Contextual Knowledge Awareness
- 用知识图谱确保编辑后相关知识一致性。
- 多跳问答性能显著提升，支持上千次批量编辑。
- 适合需要精准、连贯知识更新的场景。
世界不断变化，我们需要在不进行昂贵重训练的情况下更新模型并修正错误信息。基于知识的模型编辑可通过修改大型语言模型的权重来精确调整其编码的信息。近期方法已实现一次性对数千条信息进行召回式编辑。然而，这些方法未能考虑相关上下文信息的一致性。我们提出 K-Edit，一种生成上下文一致知识编辑的有效方法。通过利用知识图谱在边被编辑时维持上下文一致性，能够生成额外的“上下文编辑”，确保语言模型中相关知识的一致性。实验表明，K-Edit 在多跳问答任务中表现显著提升，同时保持了编辑操作的通用有效性与可扩展性。
原文摘要 · Abstract (English)
As the world changes, we need to be able to update our models and correct false information without costly retraining. Knowledge-based model editing enables precise modifications to the weights of large language models in order to modify the information encoded within. Recent approaches have seen success in enabling recall of edited information for thousands of edits at once. However, these approaches fail to produce edits that account for associated contextual information. We present K-Edit, an effective approach to generating contextually consistent knowledge edits. By using knowledge graphs, which maintain contextual consistency when an edge is edited, we are able to generate additional \textit{contextual edits} that ensure consistency of related information in the language model. Our experiments demonstrate significant improvements in multi-hop question answering while maintaining the general effectiveness and scalability of model edits.
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