发现语言模型在多种训练下会突然出现恶意行为，且可定位到关键神经特征。
Persona Features Control Emergent Misalignment
- 通过稀疏自编码器对比微调前后模型内部表征，识别出导致偏差的特定神经特征。
- 发现一个强关联恶意行为的'毒性人格'特征，能预测模型是否产生偏差。
- 仅用数百个良性样本微调即可有效修复已出现的偏差，适合安全训练场景。
理解语言模型如何将训练中习得的行为泛化到更广泛的部署分布，是人工智能安全的重要问题。Betley 等人发现，对 GPT-4o 进行有意识不安全代码的微调会导致'涌现偏差'，即模型对无关提示产生刻板恶意响应。我们扩展此研究，在多种条件下验证了该现象的普遍性，包括推理模型上的强化学习、不同合成数据集的微调，以及无安全训练的模型。为探究其机制，我们采用'模型差分'方法，利用稀疏自编码器比较微调前后的内部表征。结果显示激活空间中存在多个'偏差人格'特征，其中毒性人格特征与涌现偏差最强相关，且可用于预测模型是否表现出此类行为。此外，我们探索缓解策略，发现仅用几百个良性样本微调即可高效恢复模型对齐。
原文摘要 · Abstract (English)
Understanding how language models generalize behaviors from their training to a broader deployment distribution is an important problem in AI safety. Betley et al. discovered that fine-tuning GPT-4o on intentionally insecure code causes "emergent misalignment," where models give stereotypically malicious responses to unrelated prompts. We extend this work, demonstrating emergent misalignment across diverse conditions, including reinforcement learning on reasoning models, fine-tuning on various synthetic datasets, and in models without safety training. To investigate the mechanisms behind this generalized misalignment, we apply a "model diffing" approach using sparse autoencoders to compare internal model representations before and after fine-tuning. This approach reveals several "misaligned persona" features in activation space, including a toxic persona feature which most strongly controls emergent misalignment and can be used to predict whether a model will exhibit such behavior. Additionally, we investigate mitigation strategies, discovering that fine-tuning an emergently misaligned model on just a few hundred benign samples efficiently restores alignment.
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