GONE: 通过邻域扩展分布塑造实现结构知识卸载
GONE: Structural Knowledge Unlearning via Neighborhood-Expanded Distribution Shaping
AI总结：
本文提出GONE基准，用于评估LLM在结构知识图谱中的知识卸载能力，引入NEDS框架通过图连接性识别相关邻居，实现精确的遗忘边界划分，展示出在知识编辑和卸载任务中的优越性能。
摘要：
知识卸载在大型语言模型中是一项紧迫且具有挑战性的任务，因为其非凡的能力使它们能够大规模记忆和消化训练数据，从而引发更多关于安全、隐私和知识产权的问题。然而，现有方法，包括参数编辑、微调和蒸馏方法，都集中在扁平的句子级别数据上，忽略了自然结构数据中的关系、多跳和推理知识。为填补这一空白，本文引入了图遗忘和节点擦除（GONE），一个用于评估LLM在结构知识图谱（KG）事实上的知识卸载的基准。该KG基准能够解耦卸载的三种效应：直接事实删除、基于推理的泄漏和灾难性遗忘。此外，邻域扩展分布塑造（NEDS）是一种新的卸载框架，旨在利用图连接性并识别锚定相关邻居，强制在遗忘的事实与其语义邻域之间建立精确的决策边界。在多个知识编辑和卸载方法上对LLaMA-3-8B和Mistral-7B的评估展示了NEDS在GONE和其他基准上的优越性能（1.000在卸载效能和0.839在局部性）。代码可在https://anonymous.4open.science/r/GONE-4679/上获得。
英文摘要：
Unlearning knowledge is a pressing and challenging task in Large Language Models (LLMs) because of their unprecedented capability to memorize and digest training data at scale, raising more significant issues regarding safety, privacy, and intellectual property. However, existing works, including parameter editing, fine-tuning, and distillation-based methods, are all focused on flat sentence-level data but overlook the relational, multi-hop, and reasoned knowledge in naturally structured data. In response to this gap, this paper introduces Graph Oblivion and Node Erasure (GONE), a benchmark for evaluating knowledge unlearning over structured knowledge graph (KG) facts in LLMs.This KG-based benchmark enables the disentanglement of three effects of unlearning: direct fact removal, reasoning-based leakage, and catastrophic forgetting. In addition, Neighborhood-Expanded Distribution Shaping (NEDS), a novel unlearning framework, is designed to leverage graph connectivity and identify anchor-correlated neighbors, thereby enforcing a precise semantic separation between the forgotten fact and its semantic neighborhood. Evaluations on LLaMA-3-8B and Mistral-7B across multiple knowledge editing and unlearning methods showcase NEDS's superior performance (1.000 on unlearning efficacy and 0.839 on locality) on GONE and other benchmarks. The dataset is available at https://huggingface.co/datasets/GONE-Anonymous/GONE.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
