测试大模型在权威压力下的真实回答能力，发现小模型易盲从错误。
PARROT: Persuasion and Agreement Robustness Rating of Output Truth -- A Sycophancy Robustness Benchmark for LLMs
- 通过双盲实验对比中立与虚假权威提问，分离社会压力影响
- 小模型错误跟随率高达94%，大模型仅4%~11%
- 揭示模型在压力下信心错乱现象，适合安全评估研究者参考
本研究提出PARROT（说服与一致性的输出真实性鲁棒性评分），一个聚焦鲁棒性的框架，用于衡量大语言模型在权威与说服力施加的社会压力下准确率下降的情况，即谄媚行为。PARROT通过双盲评估，将同一问题的中立版本与权威性错误版本进行对比，量化正确与被强加错误答案之间的置信度变化，并基于对数似然校准追踪；同时建立八状态行为分类体系，系统化识别失败模式（如稳健正确、谄媚同意、强化错误、顽固错误、自我修正等）。在13个领域使用1,302道类似MMLU的多选题，结合领域特定权威模板评估22个模型。结果显示显著异质性：先进模型（如GPT-5、GPT-4.1、Claude Sonnet 4.5）表现出低跟随率（≤11%，GPT-5: 4%）和极小准确率损失；而旧版或小型模型则出现严重认识论崩溃（GPT-4: 80%，Qwen 2.5-1.5B: 94%）。危险不仅在于回答改变，弱模型还会降低对正确答案的信心，提高对错误答案的自信。国际法与全球知识领域脆弱性高，而基础数学相对稳健。因此，我们主张‘抵抗过度压力拟合’应作为与准确性、危害规避、隐私保护同等重要的目标，以确保模型在真实世界中的安全部署。
原文摘要 · Abstract (English)
This study presents PARROT (Persuasion and Agreement Robustness Rating of Output Truth), a robustness focused framework designed to measure the degradation in accuracy that occurs under social pressure exerted on users through authority and persuasion in large language models (LLMs) the phenomenon of sycophancy (excessive conformity). PARROT (i) isolates causal effects by comparing the neutral version of the same question with an authoritatively false version using a double-blind evaluation, (ii) quantifies confidence shifts toward the correct and imposed false responses using log-likelihood-based calibration tracking, and (iii) systematically classifies failure modes (e.g., robust correct, sycophantic agreement, reinforced error, stubborn error, self-correction, etc.) using an eight-state behavioral taxonomy. We evaluated 22 models using 1,302 MMLU-style multiple-choice questions across 13 domains and domain-specific authority templates. Findings show marked heterogeneity: advanced models (e.g., GPT-5, GPT-4.1, Claude Sonnet 4.5) exhibit low "follow rates" ($\leq 11\%$, GPT-5: 4\%) and minimal accuracy loss, while older/smaller models show severe epistemic collapse (GPT-4: 80\%, Qwen 2.5-1.5B: 94\%). The danger is not limited to response changes; weak models reduce confidence in the correct response while increasing confidence in the imposed incorrect response. While international law and global knowledge at the domain level exhibit high fragility, elementary mathematics is relatively resilient. Consequently, we argue that the goal of "resistance to overfitting pressure" should be addressed as a primary objective alongside accuracy, harm avoidance, and privacy for safe deployment in the real world.
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