这篇论文揭示了大模型在多选题中不一致回答的内部机制，通过StateSwap技术证明了不同表述方式会激活不同内部状态。
研究显示大语言模型在面对支持导向和消除导向两种表述方式的多选题时，回答常不一致。研究者引入双框架协议，通过最小变化的提示词保持评估目标固定。研究发现两种框架在中间层诱导出可分离的[STATE]激活。交换这些激活能系统性地改变预测结果，提高跨框架一致性。
StateSwap: Probing Support-Elimination Hidden States in Multiple-Choice Questions
Large language models often answer the same multiple-choice question inconsistently when it is posed under support-oriented and elimination-oriented framings. We investigate whether these discrepancies arise from different internal representations induced by the two framings. We introduce a dual-framing protocol with minimally varied prompts that use either support- or elimination-oriented framing while keeping the evaluation target fixed. To probe the internal computation, we append an untrained special token, [STATE], and treat its residual-stream activation as an intervention interface. Across both models, the two framings induce separable [STATE] activations concentrated in intermediate layers. Swapping these activations between paired prompts systematically changes predictions and improves cross-framing agreement, providing intervention-based evidence that the activations are behaviorally relevant. Beyond instance-level substitution, mean-difference steering directions derived from the dual-framing contrast exhibit more bounded layer-wise responses than matched contrastive activation addition directions under the evaluated protocol.