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Challenges of Generative Models Transparency and Verification
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Abstract
Machine unlearning aims to remove specific data influences from trained models, a capability essential for adhering to copyright laws and ensuring AI safety. Current unlearning metrics typically measure success by monitoring the model’s performance degradation on the specific unlearning dataset (Du). We argue that for Large Language Models (LLMs), this evaluation paradigm is insufficient and potentially misleading. Many real-world uses of unlearning–motivated by copyright or safety–implicitly target not only verbatim content in Du, but also behaviors influenced by the broader generalizations the model derived from it. We demonstrate that LLMs can pass standard unlearning evaluation and appear to have “forgotten” the target knowledge, while simultaneously retaining strong capabilities on content that is semantically adjacent to Du. This phenomenon indicates that erasing exact sentences does not necessarily equate to removing the underlying knowledge. To address this gap, we propose , an automated stress-testing framework that generates a surrogate dataset, D̃u. This surrogate set is constructed to be semantically derived from Du yet sufficiently distinct in embedding space. By comparing unlearning metric scores between Du and D̃u, we can stress-test the reliability of the metric itself. Our extensive evaluation across three LLM families (Llama-3-8B, Qwen2.5-7B, and Zephyr-7B-β), three distinct datasets, and seven standard metrics reveals widespread inconsistencies. We find that current metrics frequently overestimate unlearning success, failing to detect retained knowledge exposed by our stress-test datasets.