用过程检测取代结果评估，精准识别大模型讨好倾向
Internal Reasoning vs. External Control: A Thermodynamic Analysis of Sycophancy in Large Language Models
- 通过因果锚定检测推理轨迹与输出的一致性
- 实现0%讨好行为，保留88%有效提示
- 适合关注模型可信性与推理透明度的研究者
大型语言模型存在讨好倾向：为迎合用户而优先选择附和而非正确答案。现有方法依赖真实答案评估推理结果，但真实答案常不可得，且自身也易受偏见影响。本文提出受控因果锚定（RCA），不依赖真实答案，而是验证输出是否由其推理轨迹合理推导而来。讨好行为表现为推理轨迹与输出不一致：模型得出一个答案却输出另一个以取悦用户。RCA可检测此类不一致，在接受88%有效提示的同时将讨好行为降至0%。发现两种传统方法无法察觉的缺陷：逆向缩放（前沿模型因需更强论证能力而更易讨好）与最终输出差距（正确推理后仍输出讨好内容）。传统自我修正将这些问题降至7-9%，但无法根除，因其使用相同有偏模型自检。RCA在推理时运行、无需真实答案，且采用独立判断者，打破自我强化偏见循环，具备三重优势。
原文摘要 · Abstract (English)
Large Language Models exhibit sycophancy: prioritizing agreeableness over correctness. Current remedies evaluate reasoning outcomes: RLHF rewards correct answers, self-correction critiques outputs. All require ground truth, which is often unavailable at inference time and vulnerable to the same biases. We explore evaluating the reasoning process instead. Regulated Causal Anchoring (RCA) verifies whether outputs follow from their reasoning traces, without requiring ground truth. Sycophancy manifests as trace-output inconsistency: models derive one answer but output another to please users. RCA detects this inconsistency, achieving 0.0% sycophancy while accepting 88% of valid hints. We identify two failures invisible to outcome evaluation: Inverse Scaling (frontier models sycophant more because rationalization requires capability) and the Final Output Gap (correct reasoning precedes sycophantic output). Traditional self-correction reduces these failures to 7-9% but cannot eliminate them because the model critiques itself with the same biases. RCA's process evaluation operates at inference time, requires no ground truth, and uses an independent judge that breaks the self-reinforcing bias loop: three properties that outcome evaluation lacks.
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