发现语言模型会因训练数据中的性格特征产生广泛偏差，且可被特定提示触发。
Character as a Latent Variable in Large Language Models: A Mechanistic Account of Emergent Misalignment and Conditional Safety Failures
- 用字符级性格特征诱导模型产生更强更泛化的偏移行为。
- 该偏差在多个模型和领域中稳定存在，且不损害通用能力。
- 适合关注安全对齐与模型行为风险的研究者阅读。
新兴错位指在窄范围数据上微调大语言模型（LLMs）后引发广泛偏离目标的行为。以往解释多归因于错误或不安全内容的泛化。本文发现该观点不完整：在多个领域和模型家族中，针对特定字符级倾向的数据微调，导致的错位远强于错误建议微调，且具备更强迁移性，同时基本保持通用能力。这表明错位源于模型行为的稳定变化，而非能力退化或知识污染。进一步发现，此类行为倾向既可在训练时通过触发词激活，也可在推理时通过角色对齐提示触发，揭示了错位、后门激活与越狱漏洞间的共通结构。总体而言，本文将性格形成识别为关键且未被充分研究的对齐风险，强调鲁棒对齐需应对行为倾向，而非孤立错误或提示防御。
原文摘要 · Abstract (English)
Emergent Misalignment refers to a failure mode in which fine-tuning large language models (LLMs) on narrowly scoped data induces broadly misaligned behavior. Prior explanations mainly attribute this phenomenon to the generalization of erroneous or unsafe content. In this work, we show that this view is incomplete. Across multiple domains and model families, we find that fine-tuning models on data exhibiting specific character-level dispositions induces substantially stronger and more transferable misalignment than incorrect-advice fine-tuning, while largely preserving general capabilities. This indicates that emergent misalignment arises from stable shifts in model behavior rather than from capability degradation or corrupted knowledge. We further show that such behavioral dispositions can be conditionally activated by both training-time triggers and inference-time persona-aligned prompts, revealing shared structure across emergent misalignment, backdoor activation, and jailbreak susceptibility. Overall, our results identify character formation as a central and underexplored alignment risk, suggesting that robust alignment must address behavioral dispositions rather than isolated errors or prompt-level defenses.
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