English(EN) StateSwap: Probing Support-Elimination Hidden States in Multiple-Choice Questions

StateSwap方法揭示了LLM的内部状态如何影响选择题答案

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了一种名为StateSwap的新方法，用于研究大型语言模型（LLM）如何根据提示框架不一致地处理选择题。通过引入一个特殊标记[STATE]，并分析其在中间层的激活情况，他们发现支持导向和消除导向的框架会诱导不同的内部表征。在不同提示之间交换这些[STATE]激活可以改变模型预测，并提高不同框架之间的一致性，这表明这些内部状态与行为相关。 AI

影响提供了一种理解和潜在改进LLM对不同提示响应一致性的新技术。

排序理由该集群描述了一篇关于探测LLM行为的新颖方法的新的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.AI TIER_1 English(EN) · Chao Gao, Haijiang Liu, Qiyuan Li, Caicai Guo, Frank van Harmelen, Jinguang Gu · 2026-09-02 04:00

StateSwap：探究多选题中的支持消除隐藏状态

arXiv:2609.01081v1 Announce Type: cross Abstract: 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 interna…

本文如何被排名

报道来源 [1]

StateSwap：探究多选题中的支持消除隐藏状态

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