让大模型推理更准更快，通过结构化输出实现知识快速精准更新
StruEdit: Structured Outputs Enable the Fast and Accurate Knowledge Editing for Large Language Models
- 用结构化三元组替代自然语言，明确可编辑的推理节点
- 单步完成知识删除与更新，准确率最高，延迟最低
- 适合需要频繁更新知识的问答系统开发者
作为问答系统的主流工具，大语言模型（LLMs）需提供最新知识。当前知识编辑方法的核心目标是定位并修改自然语言输出中的过时信息，但面临两大挑战：难以识别需编辑的词元，且修改后推理链难以保持连贯性。本文认为根源在于自然语言输出的非结构化特性。为此，提出结构化编辑（StruEdit），首先引导模型生成包含推理三元组的结构化输出，随后在单步中移除过时知识并注入新信息。实验表明，相比其他方法，StruEdit 在准确率和延迟上均表现最优。
原文摘要 · Abstract (English)
As the modern tool of choice for question answering, large language models (LLMs) are expected to deliver answers with up-to-date knowledge. To achieve such ideal question-answering systems, locating and then editing outdated knowledge in the natural language outputs is a general target of popular knowledge editing methods. However, this target is challenging, as both identifying which tokens to edit in the reasoning steps and ensuring the coherence of the revised reasoning chain are difficult tasks. We argue that these challenges stem from the unstructured nature of natural language outputs. To address the above challenges, we propose $\textbf{Stru}$ctural $\textbf{Edit}$ing ($\textbf{StruEdit}$), an improved baseline for knowledge editing. We first prompt LLMs to produce structured outputs consisting of reasoning triplets. Then, StruEdit removes any potentially outdated knowledge and efficiently refills the structured outputs with up-to-date information in a single step. Experimental results show that StruEdit consistently delivers the highest accuracy with lowest latency compared with other knowledge editing methods.
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