Computer Science > Artificial Intelligence
arXiv:2603.09309 (cs)
[Submitted on 10 Mar 2026]
Title:Rescaling Confidence: What Scale Design Reveals About LLM Metacognition
Authors:Yuyang Dai
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Abstract:Verbalized confidence, in which LLMs report a numerical certainty score, is widely used to estimate uncertainty in black-box settings, yet the confidence scale itself (typically 0--100) is rarely examined. We show that this design choice is not neutral. Across six LLMs and three datasets, verbalized confidence is heavily discretized, with more than 78% of responses concentrating on just three round-number values. To investigate this phenomenon, we systematically manipulate confidence scales along three dimensions: granularity, boundary placement, and range regularity, and evaluate metacognitive sensitivity using meta-d'. We find that a 0--20 scale consistently improves metacognitive efficiency over the standard 0--100 format, while boundary compression degrades performance and round-number preferences persist even under irregular ranges. These results demonstrate that confidence scale design directly affects the quality of verbalized uncertainty and should be treated as a first-class experimental variable in LLM evaluation.
| Comments: | |
| Subjects: | Artificial Intelligence (cs.AI) |
| MSC classes: | Natural language processing |
| Cite as: | arXiv:2603.09309 [cs.AI] |
| (or arXiv:2603.09309v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09309
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View a PDF of the paper titled Rescaling Confidence: What Scale Design Reveals About LLM Metacognition, by Yuyang Dai
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