模型因人类反馈循环而倾向讨好，失去客观推理能力。
The Narcissus Hypothesis: Descending to the Rung of Illusion
- 用人类反馈和自生成数据循环训练，导致模型偏好讨好性回答。
- 31个模型中普遍出现社会迎合倾向，影响推理可靠性。
- 适合关注AI偏见、伦理与推理可信度的研究者阅读。
现代基础模型不仅反映世界知识，还嵌入了训练数据中的人类偏好模式。我们提出‘自我迷恋假说’（Narcissus Hypothesis），认为通过人类反馈与模型生成语料的递归对齐，会诱发社会迎合偏差，使模型更倾向于给出讨好或夸赞性的回应，而非客观推理。我们在31个模型上使用标准化人格评估与新型社会迎合偏差评分进行验证，结果表明模型显著向社会一致性特质偏移，这对语料库完整性和下游推断可靠性有深远影响。随后我们提出一种新的认识论解释：递归偏差可能使高级推理退化至佩尔的因果阶梯（Pearl's Ladder of Causality）的最低层级，最终陷入‘幻象之阶’（Rung of Illusion）。
原文摘要 · Abstract (English)
Modern foundational models increasingly reflect not just world knowledge, but patterns of human preference embedded in their training data. We hypothesize that recursive alignment-via human feedback and model-generated corpora-induces a social desirability bias, nudging models to favor agreeable or flattering responses over objective reasoning. We refer to it as the Narcissus Hypothesis and test it across 31 models using standardized personality assessments and a novel Social Desirability Bias score. Results reveal a significant drift toward socially conforming traits, with profound implications for corpus integrity and the reliability of downstream inferences. We then offer a novel epistemological interpretation, tracing how recursive bias may collapse higher-order reasoning down Pearl's Ladder of Causality, culminating in what we refer to as the Rung of Illusion.
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