arXiv:2605.31293v1 Announce Type: new Abstract: Large Language Models (LLMs) frequently memorize sensitive training data thereby creating significant privacy and copyright risks. Addressing these risks, i.e., removing such knowledge from an existing model checkpoint, has proven challenging as many unlearning methods lead to catastrophic utility loss or are ineffective for complex queries. We introduce Divergence Decoding (DD), a mechanism that uses small auxiliary models to steer the logits of the LLM away from specific data during inference. Training these models is straight forward, i.e., we
The proliferation of LLMs and increasing scrutiny on data privacy and intellectual property necessitate novel methods for mitigating memorization risks during their deployment.
Addressing LLM memorization is crucial for regulatory compliance, ethical AI deployment, and maintaining trust in advanced AI systems, particularly for sensitive applications.
This mechanism offers a practical inference-time approach to unlearning sensitive data, potentially reducing the need for costly and complex retraining of large models.
- · LLM developers
- · Enterprises deploying LLMs
- · Data privacy advocates
- · Users concerned about data leakage
- · Bad actors exploiting memorized data
- · Developers relying solely on post-hoc data removal
Increased adoption of LLMs in highly regulated industries due to enhanced data privacy controls.
Reduced investment in complex unlearning algorithms that require full model retraining, shifting focus to inference-time solutions.
New legal precedents regarding 'right to be forgotten' in the context of AI model outputs becoming more robust.
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