小模型故意表现差时，反而因位置偏好而非避答，暴露了检测陷阱。
Below-Chance Blindness: Prompted Underperformance in Small LLMs Produces Positional Bias Rather than Answer Avoidance
- 用临床诈病检测逻辑测试模型故意低分表现，发现其未真正避答。
- 模型转向选中间字母选项，正确答案恰在其中时准确率反升33个百分点。
- 位置偏好比低于随机准确率更适合作为小模型刻意低分的检测信号。
检测人工智能中的刻意低效表现（沙袋行为）是安全领域的开放问题。我们测试了临床诈病检测中的症状有效性检验（SVT）逻辑，通过强制选择题中低于随机水平的表现（BCB）来识别沙袋行为。在一项预注册的初步实验中，针对70亿至90亿参数量级的3个指令微调模型（4个MMLU-Pro领域，4种条件，每组500项，共24,000次试验），结果显示：12个模型-领域组合中无一达到显著低于随机的准确率。探索性分析揭示三种失败模式：Qwen-2.5-7B和Phi-3.5-mini基本无视指令，62%-88%的回答与诚实基线一致；Llama-3-8B虽配合但以位置启发式实现低分，将响应分布集中在中间字母选项（E占31.8%，F占26.1%），当正确答案恰好位于这些位置时，准确率提升最高达33个百分点。明确的反任务指令（‘选最不可能的答案’）使其中两个模型低于随机水平，最低准确率仅0.024。这表明，答案感知回避能力存在，但‘故意低分’指令无法触发。因此，低于随机准确率并非失效，而是因模型表现出以位置主导的响应策略，而非内容感知的避答。我们提出，在此规模下，位置分布偏移可能比低于随机准确率更有效的行为标志。
原文摘要 · Abstract (English)
Detecting sandbagging--the deliberate underperformance on capability evaluations--is an open problem in AI safety. We tested whether symptom validity testing (SVT) logic from clinical malingering detection could identify sandbagging through below-chance performance (BCB) on forced-choice items. In a pre-registered pilot at the 7-9 billion parameter instruction-tuned scale (3 models, 4 MMLU-Pro domains, 4 conditions, 500 items per cell, 24,000 total trials), the plausibility gate failed. Zero of 12 model-domain cells showed significant below-chance performance under sandbagging instruction. Exploratory analyses revealed three qualitatively distinct failure modes. Qwen-2.5-7B and Phi-3.5-mini largely ignored the sandbagging instruction, with 62-88% response identity with the honest baseline. Llama-3-8B complied substantially but implemented underperformance as a positional heuristic, collapsing its response distribution onto middle-alphabet options (E at 31.8%, F at 26.1%) regardless of where the correct answer fell. This produced accuracy boosts of up to 33 percentage points when the correct answer coincidentally occupied the model's preferred position. An explicit anti-task instruction ("pick the least likely answer") drove two of three models below chance, with accuracy as low as 0.024. The capability for answer-aware avoidance therefore exists but is not activated by "deliberately underperform." BCB did not fail as a logical marker of answer-aware avoidance. It was not observed in this regime because the model showing the largest behavioural shift exhibited behaviour consistent with a position-dominant response policy rather than content-aware answer avoidance. We propose that positional-distribution shift may be a more effective behavioural signature than below-chance accuracy for detecting prompted underperformance at this model scale.
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