Researchers decouple LLM knowledge from confidence judgments
Source published ·Modelwire updated
Original coverage: arXiv cs.CL ↗·How Modelwire adds context
The development
Researchers propose a framework to fix a critical misalignment in post-trained LLMs: models gain new knowledge but retain outdated confidence judgments about what they can answer. The work introduces dual trainable channels that separately govern knowledge acquisition and metacognitive assessment, trained through three stages including knowledge-delta adaptation and policy optimization. This addresses a practical problem affecting retrieval systems, model routing, and multi-step reasoning, where false confidence or unwarranted doubt degrades performance. The approach treats feeling of knowing as a functional capability rather than consciousness, making it tractable for engineering.
Modelwire’s AI-generated summary of coverage from arXiv cs.CL.
Modelwire analysis
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The paper treats feeling of knowing as a separable, trainable capability rather than an emergent property. This means confidence calibration can be fixed independently of knowledge acquisition, which prior work on knowledge editing (like the two-hop curse paper from Nov 1) treated as coupled problems.
This directly addresses a gap exposed in recent coverage. The two-hop curse showed that models encode new facts as surface answers rather than integrated knowledge. The commitment direction work from Nov 2 revealed how early answers lock in subsequent reasoning. This paper proposes a mechanism to decouple what the model knows from what it thinks it knows, which could prevent false confidence from propagating through multi-step chains. The approach complements the Bayesian agent distillation work from Oct 31, which tackled uncertainty tracking through structured reasoning; here the focus is narrower but more directly applicable to existing post-trained models.
If the same dual-channel architecture improves performance on retrieval-augmented generation benchmarks where confidence calibration currently fails (like SQuAD or FEVER with out-of-distribution queries), the method is production-ready. If gains only hold on synthetic knowledge-delta tasks, the work remains a proof of concept without clear deployment path.
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Coverage behind this analysis
These archive entries ground the connection in our analysis. They are ordered by source publication date, with links to our coverage and the original sources.
·arXiv cs.CL
Fine-tuned facts fail reasoning because models write answers, not knowledge
Researchers have identified a fundamental mismatch in how language models store and retrieve learned facts after fine-tuning. When models acquire new information through parameter updates, they encode it as surface-level answers rather than integrated knowledge that supports multi-step reasoning. The team demonstrates that the reasoning capability itself remains intact, but the model's internal representations fail…
MentionsLarge language models · Feeling of knowing · Knowledge-delta FoK training · FoK-guided policy optimization
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Modelwire summarizes, we don’t republish. arXiv cs.CL originally reported this story as “Feeling of Knowing in Large Language Models”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.