We develop a new perspective of knowledge editing for large language models (LLMs) as decoding with constraints. We propose DeepEdit (Depth-first Search based Progressive Decoding for Knowledge Editing), a neuro-symbolic method that improves knowledge editing with better coherence of reasoning, relevance to the question, and awareness of updated knowledge. DeepEdit can be flexibly applied to all black-box LLMs: it does not require any access to the model parameters, representations, or output vocabulary distributions. DeepEdit progressively produces the high-quality reasoning steps towards effective knowledge editing. It utilizes a depth-first search to revise the LLMs' output, which improves the output's informativeness to the input question and awareness of the updated knowledge. Qualitatively, DeepEdit effectively controls LLMs to produce more succinct reasoning in accord with knowledge editing. Quantitatively, DeepEdit yields significant gains on MQuaKE, a challenging multi-hop question-answering dataset with knowledge editing. We release the source code at https://github.com/wangywUST/DeepEdit.

我们提出了一种新的知识编辑视角，称之为基于约束的解码，在大型语言模型中进行。我们提出了DeepEdit（一种基于深度优先搜索的渐进解码知识编辑的神经符号方法），该方法通过更好的推理连贯性、与问题的相关性以及对更新知识的认知来提高知识编辑。DeepEdit可以灵活应用于所有黑盒语言模型，不需要访问模型参数、表示或输出词汇分布。DeepEdit逐步产生高质量的推理步骤，以实现有效的知识编辑。它利用深度优先搜索来修订语言模型的输出，从而提高对输入问题的信息量和对更新知识的认知。在定性上，DeepEdit能够有效控制语言模型，以更简洁的推理方式进行知识编辑。在定量上，DeepEdit在具有知识编辑任务的挑战性多跳问答数据集MQuaKE上取得了显著的性能提升。我们在此https URL上发布了源代码。

DeepEdit: 知识编辑作为带约束的解码