Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms (2026.acl-long)
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| Challenge: | Large language models (LLMs) are increasingly trained on massive, heterogeneous text corpora, raising serious concerns about the unauthorised use of proprietary or personal data during model training. |
| Approach: | They propose a data-level defence that renders text unlearnable to LLMs by injecting carefully designed alignment-triggering disclaimers into the models' alignment mechanisms. |
| Outcome: | The proposed approach exploits the models’ alignment mechanisms to prevent effective learning. |
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