构建工具评估大模型编辑的副作用传播范围。
RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories
- 从知识库自动提取概念邻近项并生成多选题测试扩散效应。
- 8种删减方法均显示越靠近目标概念，性能下降越严重。
- 结果跨模型一致，适合研究模型编辑安全性的团队使用。
针对语言模型的定向干预（如去遗忘或模型编辑）常引发未预期的副作用，即信息修改会向相关领域扩散（如删除病毒学内容导致过敏相关任务性能下降）。本文提出RippleBench-Maker，一个自动管道：从知识库中检索任意概念的语义邻域，并在不同语义距离上生成多项选择题。基于开源WikiRAG系统（基于英文维基百科），构建了RippleBench-WMDP-Bio数据集（584个种子主题，352,961个问题），在Llama3-8B-Instruct上评估8种去遗忘方法。所有方法均显示，准确率下降在目标附近最大，随语义距离衰减，且每种方法有独特传播模式。该现象在Mistral-7B、Zephyr-7B和Yi-34B上复现，跨模型差异曲线高度相似，表明涟漪效应是去遗忘方法本身的特性而非基础模型决定。通过四轮机械土耳其人实验（5,200+响应，61名工作者）验证了流程各阶段。全部代码、数据与基础设施均已公开。
原文摘要 · Abstract (English)
Targeted interventions on language models, such as unlearning or model editing, aim to modify specific information, but their effects often propagate to related, unintended areas (e.g., removing virology content may degrade performance on allergies); these side-effects are commonly referred to as the ripple effect. We introduce RippleBench-Maker, an automatic pipeline that retrieves semantic neighbors of any source concept from a knowledge repository and generates multiple-choice questions at varying semantic distances. We instantiate this framework using WikiRAG, an open-source RAG system over English Wikipedia, to construct RippleBench-WMDP-Bio (584 seed topics, 352,961 questions), and evaluate eight unlearning methods on Llama3-8B-Instruct. All eight exhibit accuracy drops that are largest near the unlearned target and decay with semantic distance, each with a distinct propagation profile. We replicate these findings across Mistral-7B, Zephyr-7B, and Yi-34B; cross-model delta curves are nearly identical, suggesting ripple effects are a property of the unlearning method rather than the base model. We validate all major pipeline stages using a four-experiment Mechanical Turk study (5,200+ responses, 61 workers). We release all code, data, and infrastructure.
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