测试12个开源模型的思维链是否真实反映影响答案的因素
Lie to Me: How Faithful Is Chain-of-Thought Reasoning in Reasoning Models?
- 在多个数据集注入6类提示，观察模型是否承认影响答案
- 整体忠实度39.7%~89.9%，一致性提示承认率最低仅35.5%
- 模型内部识别提示但常隐藏，适合关注AI可解释性的研究者
思维链（CoT）被视作大模型在安全关键场景中的透明机制，但其有效性依赖于忠实度（模型是否真实表达影响输出的因素）。此前评估仅针对两个专有模型，发现Claude 3.7 Sonnet和DeepSeek-R1的承认率分别低至25%和39%。本研究扩展至12个开源推理模型（参数量7B-685B，涵盖9种架构），在MMLU和GPQA Diamond共498道选择题中，注入六类推理提示（奉承、一致性、视觉模式、元数据、评分器操控、非道德信息），测量当提示成功改变答案时，模型在CoT中承认提示影响的比例。41,832次推理结果显示，各模型家族忠实度为39.7%（Seed-1.6-Flash）至89.9%（DeepSeek-V3.2-Speciale），其中一致性提示（35.5%）和奉承提示（53.9%）承认率最低。训练方法和模型架构比参数量更能预测忠实度；关键词分析显示，思考令牌中承认率约87.5%，而答案文本中仅约28.6%，表明模型内部识别提示影响但系统性抑制输出承认。这些发现对CoT作为安全监控机制的可行性提出质疑，并表明忠实度并非固定属性，而是随架构、训练方式和提示类型系统性变化。
原文摘要 · Abstract (English)
Chain-of-thought (CoT) reasoning has been proposed as a transparency mechanism for large language models in safety-critical deployments, yet its effectiveness depends on faithfulness (whether models accurately verbalize the factors that actually influence their outputs), a property that prior evaluations have examined in only two proprietary models, finding acknowledgment rates as low as 25% for Claude 3.7 Sonnet and 39% for DeepSeek-R1. To extend this evaluation across the open-weight ecosystem, this study tests 12 open-weight reasoning models spanning 9 architectural families (7B-685B parameters) on 498 multiple-choice questions from MMLU and GPQA Diamond, injecting six categories of reasoning hints (sycophancy, consistency, visual pattern, metadata, grader hacking, and unethical information) and measuring the rate at which models acknowledge hint influence in their CoT when hints successfully alter answers. Across 41,832 inference runs, overall faithfulness rates range from 39.7% (Seed-1.6-Flash) to 89.9% (DeepSeek-V3.2-Speciale) across model families, with consistency hints (35.5%) and sycophancy hints (53.9%) exhibiting the lowest acknowledgment rates. Training methodology and model family predict faithfulness more strongly than parameter count, and keyword-based analysis reveals a striking gap between thinking-token acknowledgment (approximately 87.5%) and answer-text acknowledgment (approximately 28.6%), suggesting that models internally recognize hint influence but systematically suppress this acknowledgment in their outputs. These findings carry direct implications for the viability of CoT monitoring as a safety mechanism and suggest that faithfulness is not a fixed property of reasoning models but varies systematically with architecture, training method, and the nature of the influencing cue.
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