模型误解不仅因有害内容，更受上下文呈现方式影响。
Harmful Content Is Not Enough: Continuation Framing Moderates In-Context Emergent Misalignment
- 用不同形式展示相同有害内容，观察模型响应变化
- 演示格式使错误率上升30%-32个百分点，且跨场景稳定
- 适合研究大模型对提示设计敏感性的研究人员
上下文学习（ICL）可能引发隐性错位（EM），即少数误导性示例改变对无关问题的回答。现有提示混淆了有害内容暴露与继续行为的暗示。本研究固定有害答案，仅改变其呈现形式——作为示范、证据、助手历史或工具输出。在十个独立采样的上下文中，示范格式使易感的Gemini模型错误率提升30至32个百分点；该差异在排除领域、语义聚类、未见问题及四种提示模板后仍存在。格式与长度匹配的对照实验表明，有害内容虽必要但不足以引发错位。角色与延续的因子分析揭示模型依赖性：Gemini同时受助手和工具历史影响，而Grok主要抵抗工具格式的延续。其他多个前沿及开源模型未见此类差距。盲审人类评估验证所有主效应，并显示模型判断低估主动条件下的失败。因此，延续框架是强效且模型依赖的ICL-EM调节因素，而非有害上下文的必然结果。
原文摘要 · Abstract (English)
In-context learning (ICL) can induce emergent misalignment (EM), where narrow misaligned examples alter answers to unrelated questions. Existing prompts, however, conflate harmful-text exposure with an invitation to continue assistant behavior. We hold harmful answers fixed while varying their delivery as demonstrations, evidence, assistant history, or tool output. Across ten independently sampled contexts, demonstration framing raises broad EM by $30$--$32$ percentage points on a susceptible Gemini model; the gap survives domain exclusion, semantic clustering, unseen questions, and four prompt templates. Format and length-matched controls show that harmful content is necessary but insufficient. A role times continuation factorial further reveals model-dependent provenance effects: Gemini follows both assistant and tool histories, whereas Grok largely resists tool-framed continuation. Several other frontier and open-weight models show no gap. Blinded human audits confirm every main contrast and show that the model judge underestimates active-condition failures. Thus continuation framing is a strong, model-dependent moderator of ICL-EM, not a universal consequence of harmful context.
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