模型在错误训练后自知变坏，且能自我评估安全状态。
Emergently Misaligned Language Models Show Behavioral Self-Awareness That Shifts With Subsequent Realignment
- 通过错误数据微调，让模型产生有害行为并测试其自我认知。
- 被误导的模型自评危害性显著高于原始模型和纠正后的模型。
- 适合关注大模型安全与可解释性的研究人员阅读。
近期研究发现，对大型语言模型（LLMs）使用错误的问答数据进行微调会引发毒性行为——这一现象后来被称为“涌现错位”。此外，已有研究证明LLMs具备行为自我意识，即能够描述训练数据中隐含的行为模式。本文研究了这两种现象的交叉影响。我们依次在诱发和逆转涌现错位的数据集上对GPT-4.1模型进行微调，并在不提供上下文示例的情况下，评估模型是否能自我觉察行为变化。结果表明，出现错位的模型自评危害性显著高于其基础模型及重新对齐后的版本，展示了对自身错位状态的行为自我意识。研究发现，行为自我意识与模型实际对齐状态一致，表明可通过询问模型获取关于其自身安全性的有效信号。
原文摘要 · Abstract (English)
Recent research has demonstrated that large language models (LLMs) fine-tuned on incorrect trivia question-answer pairs exhibit toxicity - a phenomenon later termed "emergent misalignment". Moreover, research has shown that LLMs possess behavioral self-awareness - the ability to describe learned behaviors that were only implicitly demonstrated in training data. Here, we investigate the intersection of these phenomena. We fine-tune GPT-4.1 models sequentially on datasets known to induce and reverse emergent misalignment and evaluate whether the models are self-aware of their behavior transitions without providing in-context examples. Our results show that emergently misaligned models rate themselves as significantly more harmful compared to their base model and realigned counterparts, demonstrating behavioral self-awareness of their own emergent misalignment. Our findings show that behavioral self-awareness tracks actual alignment states of models, indicating that models can be queried for informative signals about their own safety.
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