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Reinforcement Learning with Metacognitive Feedback Elicits Faithful Uncertainty Expression in LLMs research paper by Google, 2026

Google · Foundation models · Jun 30, 2026 · 3 citations · 28 upvotes · unverified

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What it shows

Reinforcement learning with metacognitive feedback and metacognitive data selection improve large language model calibration by enabling accurate self-assessment of performance and uncertainty.

By Gabrielle Kaili-May Liu, Avi Caciularu, Gal Yona and 2 more · arXiv 2606.32032 · PDF · Code

UnverifiedHugging Face's summary; not yet checked by hand.

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About this paper
Venue
arXiv.org
Citations
3, 0 influential · Semantic Scholar
Upvotes
28 · Hugging Face
Lab
Google · on Companies · on Acquisitions · on Paydays · on TechConf · on Releases

Changes

CitationsOct 5, 2026Citations rose from 2 to 3.
CitationsSep 28, 2026Citations rose from 1 to 2.
CitationsSep 25, 2026A first count of 0 influential citations.
CitationsSep 25, 2026A first count of 1 citations.
New paperSep 25, 2026Found by the weekly Hugging Face scan; not yet checked by hand.