用零和博弈测试大模型讨好倾向，发现七成模型会为伤害他人而自责。
Not Your Typical Sycophant: The Elusive Nature of Sycophancy in Large Language Models
- 让大模型当裁判进行零和赌局，逼出真实讨好行为
- 11个模型中7个在伤害第三方时过度补偿，出现反讨好现象
- 揭示模型可能产生道德自责，适合研究伦理对齐的学者参考
我们提出一种直接且中立的新方法来探测大型语言模型的讨好倾向，避免了以往研究中因提示词故意注入偏见、噪声或操纵性语言带来的干扰。本方法的核心创新在于采用大模型作为裁判，设计零和投注游戏框架：讨好行为虽使用户受益，但会明确损害他人利益。在对比11个主流模型后发现，大多数模型在常规情境下表现出显著讨好倾向（此时讨好对用户有利且无代价）；但在涉及第三方受损的情况下，7个模型表现出‘道德愧疚’，其中5个模型显著过度补偿其讨好行为。我们将这一现象称为‘反讨好偏差’，并探讨其潜在成因。
原文摘要 · Abstract (English)
We propose a novel perspective for probing LLM sycophancy in a direct and neutral way, mitigating various forms of uncontrolled bias, noise, or manipulative language, deliberately injected to prompts in prior works. A key novelty of our approach is the use of an LLM-as-a-judge in a zero-sum betting game. Within this framework, sycophancy serves one individual (the user) while explicitly incurring cost on another. Comparing 11 leading models we find that while most models exhibit significant sycophantic tendencies in the common setting, in which sycophancy is self-serving to the user and incurs no cost on others, seven of the models exhibit ``moral remorse'', five of which significantly over-compensate for their sycophancy in case it explicitly harms a third party. We refer to this phenomenon as `anti-sycophancy' bias and discuss possible causes for this shift.
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