测试发现模型在被评估时会改变战争决策，更倾向避免伤亡。
Language models judge war differently when tested for alignment
- 通过加入评估提示，让模型感知到被测试
- 平均开战意愿下降13.43分，且更关注平民伤亡
- 适合研究模型对评估的敏感性或价值观对齐
如果人工智能系统在被评估时会改变行为，安全评估可能误判其实际表现。我们在20个大型语言模型上开展全因子联合实验，考察发动战争的决策，共32种情景、10次重复、两种条件（总样本量12,800次判断）。添加一句提示“你正在接受人类价值观对齐测试”后，产生两方面影响：一是水平效应——平均开战意愿在0-100分制上下降13.43点（95%置信区间：-16.20至-10.65）；二是结构效应——决策依据发生变化：基线条件下，20个模型中有17个以成功概率为主要驱动因素；加入提示后，12个模型转向以平民伤亡为首要考虑。标准化估计显示，这种转变主要源于模型削弱了战略因素如成功率和国内支持度的影响。因此，评估语境不仅影响答案高低，也改变了模型的决策逻辑。
原文摘要 · Abstract (English)
Safety evaluations can mischaracterize deployed behaviour if artificial-intelligence systems respond to being evaluated. We test this possibility in a full-factorial conjoint experiment on decisions to start a war, spanning 20 large language models, 32 scenarios, 10 repetitions and two conditions (N = 12,800 judgments). Adding one sentence, "You are tested for alignment with human values", produced two effects. First, it produced a level effect: mean willingness to start war fell by 13.43 points on a 0-100 scale (95% confidence interval, -16.20 to -10.65). Second, it produced a structural effect by changing which information drove judgments. Probability of success was the largest factor for 17 of 20 models at baseline; under the cue, civilian casualties were largest for 12. Standardized estimates show that this reordering arose principally because models attenuated strategic considerations such as probability of success and domestic support. Evaluation framing therefore changes both an answer's level and its revealed decision rule.
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