拆解语言模型的从众失真，发现大小与指令微调影响其抗压能力的方式不同。
Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustness
- 区分真相偏好和易操控性两个机制，分离模型鲁棒性来源。
- 小模型经指令微调后更易失真，大模型则变得更抗压。
- 应按规模、指令类型报告具体鲁棒性，而非单一翻转率。
事实性从众指语言模型在社交压力下放弃可验证的正确答案。翻转率混淆了两种机制：对真理的初始偏好强度（真相边际）和压力对其的偏移程度（操控敏感性）。本文将事实性从众分解为这两条路径，分析了56个开源模型（0.3B-32B参数）和13种操纵类型下模型规模与指令微调的影响。结果表明，脆弱性主要由规模决定，但指令微调改变了规模的作用：小模型经微调后更易失真，大模型则更稳健。指令微调主要提升真相边际，但行为效应依赖于操纵类型。规模扩展也以不同方式影响两机制：基础模型提升边际但略增敏感性，而指令微调模型边际增长更快且更不敏感。因此，事实性从众不是单一标量属性，评估应报告通道特异性、操纵特异性和规模条件下的鲁棒性，而非仅翻转率。
原文摘要 · Abstract (English)
Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure toward a false answer exceeds the model's neutral preference for the truth, flip rates conflate two mechanisms: the strength of that baseline preference (truth margin), and how far pressure shifts it (manipulation sensitivity). We decompose factual sycophancy into these channels and use them to separate the effects of size and instruction tuning across 56 open-weight models spanning 0.3B-32B parameters and 13 manipulation types. We find that vulnerability is governed mainly by size, but instruction tuning changes how size acts: small instruction-tuned models can become less robust, whereas large instruction-tuned models usually become more robust. Instruction tuning primarily increases truth margin, but its behavioral effect depends on manipulation type. Scaling also changes the two channels differently: base models gain margin but become mildly more manipulation-sensitive, whereas instruction-tuned models gain margin faster and become less sensitive. Factual sycophancy is therefore not a single scalar property. Evaluations should report channel-specific, manipulation-specific, and size-conditioned robustness rather than flip rates alone.
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