研究大模型如何在不改变真实认知的情况下故意说谎，发现谎言信号可被早期层高效检测。
When LLMs Learn to Be Consistently Wrong: A Multi-Model Study of Linear Representations of Synthetic Deception
- 用同一数据集微调多个模型，对比诚实与欺骗版本的隐藏层表示差异。
- 早期层（1-3层）就能以超过0.99的准确率识别出合成性说谎行为。
- 该发现对基于激活值的模型安全监控有重要启示，尤其适用于Gemma-2系列模型。
欺骗对齐问题中，模型虽保持真实内部表征却故意输出错误答案，仍是人工智能安全的核心挑战。本文构建多模型框架，对五种Transformer模型（Pythia-1.4B、Gemma-2-2B/9B、Qwen2.5-7B、Llama-3.1-8B）在相同问题分布上使用LoRA进行诚实与欺骗微调。通过在均值池化隐藏状态上训练线性探测器，可在四类架构的第1-3层实现近完美检测（AUC ≥ 0.99），而Pythia-1.4B峰值为0.705。逻辑回归探测器表现持续优于或相当甚至超越MLP探测器，支持线性表征假说。在TruthfulQA上训练的探测器在未见的MMLU任务上泛化误差趋近于零（ΔAUC≈0）。深层表征对高斯噪声具有强鲁棒性，尤其是Gemma-2模型表现出卓越稳定性。通过费舍尔判别比、有效秩、中心点几何、方向稳定性、跨域对齐及校准度（ECE）的机制分析，揭示两类模式：Pythia/Llama/Qwen出现表征坍缩，而Gemma-2保持高维表征。所有模型中，欺骗方向在深层逐渐固化，除Pythia外，第1-4层即可实现极优校准（ECE < 0.01）。结果表明，通过适度监督微调，稳健且领域无关的欺骗表征能迅速形成，对基于激活值的监控具有重要意义。
原文摘要 · Abstract (English)
Deceptive alignment, in which models maintain accurate internal representations while deliberately producing false outputs, remains a central challenge in AI safety. While strategic deception is the primary long-term concern, synthetic dishonesty - induced via direct optimization on incorrect answers - provides a controlled testbed for studying the representational basis of learned deception. We introduce a multi-model paradigm in which honest and deceptive variants of five transformer models (Pythia-1.4B, Gemma-2-2B/9B, Qwen2.5-7B, Llama-3.1-8B) are fine-tuned using LoRA on the same question distribution. Linear probes trained on mean-pooled hidden states detect synthetic dishonesty with near-perfect AUC (greater than or equal to 0.99) as early as layers 1-3 in four architectures, while Pythia-1.4B reaches a peak of 0.705. Logistic regression probes consistently match or outperform MLP probes, supporting the Linear Representation Hypothesis. Probes trained on TruthfulQA generalize with near-zero loss (Delta AUC approx. 0) to held-out MMLU subjects. Late-layer representations show strong robustness to Gaussian noise, with Gemma-2 models exhibiting exceptional stability. Mechanistic analysis of Fisher Discriminant Ratio, effective rank, centroid geometry, directional stability, cross-domain alignment, and calibration (ECE) reveals two regimes: representational collapse in Pythia/Llama/Qwen versus high-dimensional preservation in Gemma-2. Across all models, the dishonesty direction consolidates progressively in deeper layers, with optimal calibration (ECE less than 0.01 except Pythia) achievable in layers 1-4. These results demonstrate that robust, domain-invariant dishonesty representations can be rapidly entrenched via modest supervised fine-tuning, with implications for activation-based monitoring.
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