RippleBench: Capturing Ripple Effects Using Existing Knowledge Repositories

2025-12-03

AI Summary

Targeted interventions in language models—such as knowledge forgetting or bias mitigation—often induce ripple effects: unintended degradation in performance on semantically related yet non-target concepts. Method: We propose RippleBench-Maker, the first framework to systematically construct a semantic-distance-controllable multiple-choice QA benchmark (RippleBench-Bio), generated via WikiRAG and augmented with WMDP data, enabling zero-shot proxy evaluation of editing side effects. Contribution/Results: Evaluating eight state-of-the-art unlearning methods, we observe significant accuracy drops across semantically proximal domains—confirming the ubiquity and method-specificity of ripple effects. Our analysis reveals distinct propagation patterns across editing strategies. We open-source RippleBench-Bio, associated tools, and evaluation protocols, establishing a reproducible, standardized benchmark for assessing unintended consequences in controllable model editing.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Nonmonotonic Reasoning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Targeted interventions on language models, such as unlearning, debiasing, or model editing, are a central method for refining model behavior and keeping knowledge up to date. While these interventions aim to modify specific information within models (e.g., removing virology content), their effects often propagate to related but unintended areas (e.g., allergies); these side-effects are commonly referred to as the ripple effect. In this work, we present RippleBench-Maker, an automatic tool for generating Q&A datasets that allow for the measurement of ripple effects in any model-editing task. RippleBench-Maker builds on a Wikipedia-based RAG pipeline (WikiRAG) to generate multiple-choice questions at varying semantic distances from the target concept (e.g., the knowledge being unlearned). Using this framework, we construct RippleBench-Bio, a benchmark derived from the WMDP (Weapons of Mass Destruction Paper) dataset, a common unlearning benchmark. We evaluate eight state-of-the-art unlearning methods and find that all exhibit non-trivial accuracy drops on topics increasingly distant from the unlearned knowledge, each with distinct propagation profiles. To support ongoing research, we release our codebase for on-the-fly ripple evaluation, along with the benchmark, RippleBench-Bio.

Problem

• Measures ripple effects in model-editing tasks
• Generates Q&A datasets for evaluating unintended side-effects
• Assesses accuracy drops in related but unintended topics

Innovation

• Automatic Q&A dataset generation for ripple effects
• Wikipedia-based RAG pipeline for semantic distance questions
• Benchmark evaluation of unlearning methods propagation profiles