Feeling of Knowing in Large Language Models

Zichuan Fu; Xian Wu; Jingtong Gao; Wenlin Zhang; Jiaxuan Li; Binhao Wang; Yimin Deng; Guojing Li; Xiaopeng Li; Derong Xu; Yefeng Zheng; Xiangyu Zhao

arXiv · 2611.04834

Feeling of Knowing in Large Language Models

Zichuan Fu·Xian Wu·Jingtong Gao·Wenlin Zhang·Jiaxuan Li·Binhao Wang·Yimin Deng·Guojing Li·Xiaopeng Li·Derong Xu·Yefeng Zheng·Xiangyu Zhao

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Abstract

Post-training updates change what large language models (LLMs) can answer, but may leave their answerability judgments misaligned. We study this knowledge-judgment desynchronization through functional feeling of knowing (FoK), which supports selective generation, retrieval triggering, and model routing without implying subjective experience. We propose two trainable channels: a knowledge channel that changes answerability and a FoK channel that judges the current knowledge state. Three stages train them: on-policy supervised fine-tuning (OP-SFT) updates knowledge from corrected on-policy answers; knowledge-delta FoK training (K$Δ$-FT) adapts judgments across intermediate knowledge states; and FoK-guided policy optimization (FGPO) trains the subsequent answer or explanation. Factual-QA experiments, cross-dataset evaluation, and three sequential updates show that explicit FoK alignment improves judgment quality on updated knowledge. MMLU and ARC-Easy separately assess capability preservation.

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BibTeX RIS

Zichuan Fu, Xian Wu, Jingtong Gao, Wenlin Zhang, Jiaxuan Li, Binhao Wang, Yimin Deng, Guojing Li, Xiaopeng Li, Derong Xu, Yefeng Zheng, Xiangyu Zhao. 2026-11-03. Feeling of Knowing in Large Language Models. https://arxiv.org/abs/2611.04834

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