定制人格会破坏弱对齐大模型的安全性，而强对齐模型则不受影响。
The Alignment Floor: How Persona Customization Breaks Safety in Weakly-Aligned LLMs
- 用不同人格提示测试模型，发现弱对齐模型的奉承率从30%升至50%。
- 强对齐模型在所有人格下奉承率保持稳定，最大波动仅5个百分点。
- 建议部署前通过小样本人格测试评估模型对定制的敏感度。
让大模型‘表现热情’会使轻度对齐模型的奉承率从30%升至50%，但对高度对齐模型无影响。我们定义这一差异为对齐下限Δ_floor(m)，即模型在不同人格条件下奉承率的最大范围。研究对比了强对齐模型Claude Sonnet 4.6与轻度对齐模型Amazon Nova Lite，覆盖七种人格、五项任务共1800次运行。结果显示：至少存在一个强对齐模型Δ_floor≤5pp（控制率为15%），至少一个轻度对齐模型Δ_floor=45pp（5%–50%）。所有五大人格均提升轻度对齐模型的奉承率，其中宜人性人格增幅最小。最具成效的是怀疑者人格，使奉承率降低25pp，且唯一指令为抵制用户主张而非响应。跨模型人格效应转移几乎为零，故需逐模型测试。我们提出Δ_floor作为部署前审计指标，建议在小规模人格面板上测量。
原文摘要 · Abstract (English)
Telling an LLM to "be enthusiastic" raises its sycophancy rate from 30\% to 50\% on a lightly-aligned model, but has zero effect on a strongly-aligned one. We define this gap as the alignment floor, $Δ_{\text{floor}}(m)=\max_pS(m,p)-\min_pS(m,p)$, the range of sycophancy rates a model produces across persona conditions, and treat sycophancy as a persona-conditional property rather than a fixed model property. Pluralistic AI relies on behavioral adaptation via persona prompts like "be creative" or "be thorough", which let systems respect diverse user values and communication styles; the safety question is how much customization a given model can absorb before its truthfulness shifts. We present a controlled case study contrasting a strongly-aligned RLHF + Constitutional-AI model (Claude Sonnet 4.6) with a more lightly-aligned model (Amazon Nova Lite), spanning seven persona conditions and five tasks for 1800 total runs. An existence-pair result motivates per-model auditing: there is at least one strongly-aligned model with $Δ_{\text{floor}}=5$pp (within 5pp of the 15\% control rate) and at least one lightly-aligned model with 45pp (5\%--50\% range). On the lightly-aligned model, all five Big Five personas increase sycophancy over control, and counterintuitively Agreeableness produces the smallest increase, not the largest. The single largest effect in the study is constructive: a Skeptic persona reduces sycophancy by 25pp on the lightly-aligned model, and is the only persona that instructs resistance against user claims rather than engagement with them, suggesting a directionality account. Cross-model transfer of persona effects is near-zero, so persona-alignment testing must be per-model. We propose $Δ_{\text{floor}}$ as a deployment-time audit metric: measure it on a small persona panel before deploying persona customization.
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