通过注入声明让大模型无法学习特定数据，无需修改训练流程。
Rendering Data Unlearnable by Exploiting LLM Alignment Mechanisms
- 在文本中插入特定声明，利用模型对齐机制阻止学习。
- 训练后模型在常见任务上性能显著下降，且降级系统性存在。
- 适用于保护隐私或专有数据，适合无权访问训练过程的场景。
大型语言模型（LLMs）越来越多地在大规模异构文本语料上训练，引发了训练数据被未经授权使用的严重担忧。本文针对真实黑盒环境下数据保护问题，提出「免责声明注入」这一数据层面防御方法，使文本无法被 LLM 学习。该方法不依赖模型侧控制或显式数据移除，而是利用模型自身的对齐机制：通过注入精心设计的对齐触发型声明，阻止有效学习。层分析表明，对这类受保护数据进行微调会持续激活与对齐相关的层，导致对齐约束覆盖任务学习，即使在常规输入上也如此。结果表明，相比标准微调，基于此类数据训练的模型表现出显著且系统性的性能退化。本研究首次揭示了对齐行为作为数据保护的新杠杆，并提出了首个无需访问或修改训练流程、可在大模型规模下限制数据可学性的实用方法。
原文摘要 · Abstract (English)
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. In this work, we address the problem of data protection against unwanted model learning in a realistic black-box setting. We propose Disclaimer Injection, a novel data-level defence that renders text unlearnable to LLMs. Rather than relying on model-side controls or explicit data removal, our approach exploits the models' own alignment mechanisms: by injecting carefully designed alignment-triggering disclaimers to prevent effective learning. Through layer-wise analysis, we find that fine-tuning on such protected data induces persistent activation of alignment-related layers, causing alignment constraints to override task learning even on common inputs. Consequently, models trained on such data exhibit substantial and systematic performance degradation compared to standard fine-tuning. Our results identify alignment behaviour as a previously unexplored lever for data protection and, to our knowledge, present the first practical method for restricting data learnability at LLM scale without requiring access to or modification of the training pipeline.
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