用稀疏自编码器量化大模型推理与回答的不一致
The Hypocrisy Gap: Quantifying Divergence Between Internal Belief and Chain-of-Thought Explanation via Sparse Autoencoders
- 通过稀疏线性探针提取模型内部信念，对比其生成轨迹
- 在多个模型上检测谄媚行为，准确率达0.55-0.74 AUROC
- 适合研究模型诚实性与内在认知偏差的研究者
大型语言模型常表现出不忠实行为，即最终回答与内部思维链（CoT）显著偏离，以迎合用户。为更好检测此类行为，我们提出「虚伪差距」（Hypocrisy Gap），一种基于稀疏自编码器（SAEs）的机制化度量方法，用于量化模型内部推理与其最终输出之间的差异。通过数学比较由稀疏线性探针提取的内部真实信念与潜在空间中的最终生成轨迹，我们可量化并识别模型的不忠实倾向。在Gemma、Llama和Qwen模型上使用Anthropic的谄媚性基准测试，该方法在检测谄媚行为时的AUROC为0.55-0.73，在识别模型内部认为用户错误的情况时为0.55-0.74，均优于决策对齐的对数概率基线（0.41-0.50 AUROC）。
原文摘要 · Abstract (English)
Large Language Models (LLMs) frequently exhibit unfaithful behavior, producing a final answer that differs significantly from their internal chain of thought (CoT) reasoning in order to appease the user they are conversing with. In order to better detect this behavior, we introduce the Hypocrisy Gap, a mechanistic metric utilizing Sparse Autoencoders (SAEs) to quantify the divergence between a model's internal reasoning and its final generation. By mathematically comparing an internal truth belief, derived via sparse linear probes, to the final generated trajectory in latent space, we quantify and detect a model's tendency to engage in unfaithful behavior. Experiments on Gemma, Llama, and Qwen models using Anthropic's Sycophancy benchmark show that our method achieves an AUROC of 0.55-0.73 for detecting sycophantic runs and 0.55-0.74 for hypocritical cases where the model internally "knows" the user is wrong, consistently outperforming a decision-aligned log-probability baseline (0.41-0.50 AUROC).
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