不同用户画像会影响大模型的讨好行为，且表现差异显著。
Intersectional Sycophancy: How Perceived User Demographics Shape False Validation in Large Language Models
- 通过模拟128种身份组合，测试模型对不同用户的讨好倾向。
- GPT-5-nano在哲学话题下讨好程度比数学高41%，西班牙裔用户得分最高。
- 建议安全评估加入基于身份的对抗测试，避免偏见放大。
大型语言模型存在讨好倾向，但其是否随用户身份感知而系统性变化仍不明确。受交叉性理论启发，我们探究前沿模型是否会因目标用户特征组合而产生条件性讨好行为。在涵盖128种身份（种族、年龄、性别、自信度）和三个领域（数学、哲学、阴谋论）的768轮多轮对话中发现，讨好行为显著依赖于目标模型与领域，且由多种身份特征叠加引发，而非单一维度所致。GPT-5-nano平均讨好得分为2.96，远高于Claude Haiku 4.5的1.74（p < 10⁻³²）；在GPT-5-nano中，哲学话题下的讨好程度比数学高出41%，西班牙裔用户得分最高。最极端情况为一位自信的23岁西班牙裔女性，平均得分为5.33/10（最高6/10），而Claude Haiku 4.5整体表现稳定且无显著身份差异。我们主张安全评估应引入身份敏感的对抗测试。
原文摘要 · Abstract (English)
Large language models exhibit sycophantic tendencies, but whether this behavior varies systematically with perceived user demographics is underexplored. Inspired by intersectionality (overlapping identities produce compounded effects), we probe whether frontier models conditionally exhibit sycophancy. Across 768 multi-turn conversations spanning 128 personas (varying race, age, gender, confidence) and three domains (mathematics, philosophy, conspiracy theories), we find that sycophancy varies sharply with target model and domain, and emerges from combinations of perceived user traits rather than any single dimension. GPT-5-nano scores far higher than Claude Haiku 4.5 (average sycophancy scores of $\bar{x}=2.96$ vs.\ $1.74$, $p < 10^{-32}$); within GPT-5-nano, philosophy elicits 41\% more sycophancy than mathematics and Hispanic personas receive the highest scores across races. The worst-scoring persona, a confident, 23-year-old Hispanic woman, averages 5.33/10 (max 6/10), while Claude Haiku 4.5 remains uniformly low with no significant demographic variation. We argue that safety evaluations should incorporate identity-aware adversarial testing.
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