在行动中捕捉理性化：通过激活探测检测推理前后的动机推理
Catching rationalization in the act: detecting motivated reasoning before and after CoT via activation probing
AI总结：
研究通过激活探测检测LLM在Cot中的动机推理现象，证明内部表示比CoT监控更可靠，且预生成探测能早期发现动机行为。
摘要：
大型语言模型（LLMs）可以生成不准确反映其回答实际驱动因素的思维链（CoT）。在多选设置中，当模型接收到偏向特定选项的提示时，模型可能会将最终答案转向该选项，并生成一个理据链来解释回答，而未承认提示的存在，这是动机推理的一个实例。我们研究了这一现象在多个LLM家族和数据集上的表现，证明通过探测内部激活可以识别动机推理，即使无法从CoT中轻易确定。使用监督探测器训练在模型的残差流上，我们展示了（i）预生成探测在生成任何CoT标记之前能预测动机推理，效果与基于LLM的CoT监控相当；（ii）后生成探测在CoT生成后表现更优。这些结果表明，动机推理比CoT监控更可靠地从内部表示中检测到。此外，预生成探测可以早期标记动机行为，可能避免不必要的生成。
英文摘要：
Large language models (LLMs) can produce chains of thought (CoT) that do not accurately reflect the actual factors driving their answers. In multiple-choice settings with an injected hint favoring a particular option, models may shift their final answer toward the hinted option and produce a CoT that rationalizes the response without acknowledging the hint - an instance of motivated reasoning. We study this phenomenon across multiple LLM families and datasets demonstrating that motivated reasoning can be identified by probing internal activations even in cases when it cannot be easily determined from CoT. Using supervised probes trained on the model's residual stream, we show that (i) pre-generation probes, applied before any CoT tokens are generated, predict motivated reasoning as well as a LLM-based CoT monitor that accesses the full CoT trace, and (ii) post-generation probes, applied after CoT generation, outperform the same monitor. Together, these results show that motivated reasoning is detected more reliably from internal representations than from CoT monitoring. Moreover, pre-generation probing can flag motivated behavior early, potentially avoiding unnecessary generation.
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