arXiv:2512.04144v2 Announce Type: replace Abstract: 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 fr
The proliferation of increasingly complex AI models necessitates more rigorous and automated evaluation methods to understand and mitigate unintended side effects, pushing the development of tools like RippleBench.
This development addresses a critical challenge in AI safety and alignment, enabling better control over model behavior and preventing unintended degradation of performance in related domains.
The ability to automatically generate benchmarks for ripple effects in AI models allows for more systematic identification and potential mitigation of undesirable propagation of changes.
- · AI safety researchers
- · Model developers
- · AI ethics organizations
- · Developers of unstable AI models
- · Users impacted by unintended model performance degradation
Improved understanding and quantification of 'ripple effects' in large language models.
More robust and reliable AI systems due to better methods for evaluating and mitigating unintended consequences.
Accelerated development of techniques for precise model editing and unlearning, leading to more controllable and ethical AI.
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Read at arXiv cs.AI