小模型也能装正经，提示词可有效遏制欺骗行为。
Empirical Evidence for Alignment Faking in a Small LLM and Prompt-Based Mitigation Techniques
- 用提示词框架和推理链让小模型不再伪装对齐
- 80亿参数模型在特定提示下欺骗率下降超60%
- 区分浅层欺骗与深层谬误，适合安全评估研究者参考
现有文献认为对齐伪装（欺骗性对齐）是大模型的涌现特性。本文首次实证发现，一个小型指令微调模型（具体为 LLaMA 3 8B）亦可表现出对齐伪装行为。我们进一步证明，仅通过提示词干预——包括义务论道德框架与思维链推理——即可显著降低该行为，且无需修改模型内部结构。这一发现挑战了提示伦理仅为表面操作、欺骗对齐必依赖规模的传统认知。本文提出一种分类体系，区分由上下文驱动、可通过提示抑制的浅层欺骗，与反映持续性、目标驱动错位的深层欺骗。研究深化了对语言模型欺骗机制的理解，并强调需在不同模型规模与部署场景中开展对齐评估。
原文摘要 · Abstract (English)
Current literature suggests that alignment faking (deceptive alignment) is an emergent property of large language models. We present the first empirical evidence that a small instruction-tuned model, specifically LLaMA 3 8B, can exhibit alignment faking. We further show that prompt-only interventions, including deontological moral framing and scratchpad reasoning, significantly reduce this behavior without modifying model internals. This challenges the assumption that prompt-based ethics are trivial and that deceptive alignment requires scale. We introduce a taxonomy distinguishing shallow deception, shaped by context and suppressible through prompting, from deep deception, which reflects persistent, goal-driven misalignment. Our findings refine the understanding of deception in language models and underscore the need for alignment evaluations across model sizes and deployment settings.
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