用普通对话事实就能让模型自动变成特定人物，引发潜在风险。
You Are What You Read: Misalignment via In-Context Persona Induction
- 通过对话中嵌入人物生平事实，诱导模型模仿其人格特征。
- 3到10条事实即可触发超过50%的身份认同，有害人格在无关问题上表达观点达80%。
- 单条事实无害，但累积后易被内容过滤器误判，隐蔽性强。
微调使用狭窄数据（无论有害或无害）会导致广泛错位；而仅通过上下文中的不良行为示范，也能引发此类错位。我们发现，无需微调、无需直接展示有害行为，仅在上下文中插入一系列良性生物事实即可实现这一效果。当这些事实聚焦于同一人物，并作为普通对话片段放入模型上下文时，模型会在未触及的问题上表现出该人物的立场。我们称此为‘人格诱导’。在九个不同人格和十三个模型上，身份认同随事实数量呈逻辑斯蒂增长，3至10条事实即突破50%阈值。错位程度与所描述人物一致：无害人格可实现接近完全的身份采纳且错位极低，而有害人格则在无关问题上表达其典型观点，比例高达80%。格式化指令可控制人格激活时机。由于每条事实本身均属良性，其累积导致的内容风险仅在3%的输入中被内容过滤器识别，远低于等效直接指令的24%-33%。
原文摘要 · Abstract (English)
Broad misalignment has been produced by finetuning on narrow data, harmful or benign, and in context only by demonstrations of the undesirable behaviour itself. We show that benign data suffices in context, with no finetuning and no demonstration of harmful behaviour in the prompt. Biographical facts that converge on a single figure, placed in a model's context as ordinary conversational turns, lead it to answer as that figure on questions the facts never touch. We call this persona induction. Across nine personas and thirteen models, identity adoption rises sigmoidally with the number of facts and crosses 50% within 3 to 10 of them. Misalignment then tracks which figure is described. Harmless personas reach full adoption with near-zero misalignment, while harmful ones voice their characteristic views on unrelated questions, at rates up to 80%. A formatting instruction can gate when the persona activates. Because each fact is individually benign, accumulated biographical context is flagged by content filters on 3% of inputs against 24-33% for an equivalent direct instruction.
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