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Certain prompt methods let LLMs output well-calibrated verbalized confidence scores.

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T0 review · grok-4.3

2026-05-23 06:34 UTC

load-bearing objection Prompt choice matters for calibrated verbalized confidence in LLMs, but benchmark representativeness is the open question. the 2 major comments →

arxiv 2412.14737 v2 submitted 2024-12-19 cs.CL

On Verbalized Confidence Scores for LLMs

Daniel Yang , Yao-Hung Hubert Tsai , Makoto Yamada This is my paper
classification cs.CL
keywords verbalized confidenceuncertainty quantificationlarge language modelsprompt methodscalibrationtrustworthiness
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper examines whether large language models can assess their own uncertainty by including a numerical confidence score in their generated text. It evaluates this verbalization approach across multiple datasets, models, and prompting techniques to measure how well the reported scores align with actual response accuracy. Results indicate that calibration quality depends heavily on the specific prompt used, yet some methods produce scores that reliably reflect correctness. This matters because verbalized scores require no access to internal model states, extra sampling, or auxiliary models, offering a lightweight route to uncertainty estimates that could support better human trust and agent decision-making.

Core claim

The central claim is that verbalized confidence scores, obtained by directly prompting an LLM to state its certainty as part of its output, can be well-calibrated when the right prompt strategy is chosen, as demonstrated by consistent alignment between reported confidence levels and empirical accuracy across an extensive set of benchmarks.

What carries the argument

Verbalized confidence scores produced by the LLM itself in response to targeted prompts that request a numerical self-assessment of certainty.

Load-bearing premise

The benchmark datasets and evaluation metrics used are representative of the uncertainty that matters in downstream LLM applications.

What would settle it

Finding that the same prompt methods produce poorly calibrated scores on a new task domain or dataset outside the evaluated benchmarks would falsify the central claim.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • Verbalized scores can function as a prompt- and model-agnostic method for uncertainty quantification.
  • LLM agents can use these scores to make more informed decisions when interacting with each other.
  • Human users can place greater trust in responses that include reliable self-reported confidence.
  • The approach avoids the overhead of logit inspection or response sampling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Integration into standard chat interfaces could occur with minimal system changes.
  • The method might extend naturally to multi-turn conversations where confidence evolves.
  • Further checks on out-of-distribution inputs could clarify the boundary of reliable verbalization.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

2 major / 2 minor

Summary. The manuscript evaluates verbalized confidence scores produced by LLMs across multiple models, datasets (primarily QA and classification tasks), and prompting strategies. It concludes that certain prompt methods can yield well-calibrated scores, positioning verbalized confidence as a low-overhead, prompt- and model-agnostic uncertainty quantification technique, with code released for reproducibility.

Significance. If the calibration results prove robust, the work offers a practical alternative to logit-based or sampling-based UQ methods for LLM trustworthiness and agentic decision-making. The public code release is a clear strength that supports verification and extension.

major comments (2)
  1. [Abstract; evaluation sections (likely §4–5)] The central claim that certain prompt methods produce well-calibrated verbalized scores rests on results from standard benchmarks (QA, classification, reasoning). These datasets may under-represent the ambiguity, distribution shift, and multi-step dependencies typical of downstream LLM-agent applications; without explicit tests on such tasks, the observed calibration may not transfer (see skeptic concern on benchmark representativeness).
  2. [Abstract] Abstract states an 'extensive benchmark' but provides no details on calibration metrics (e.g., ECE definition), statistical significance tests, or data exclusion rules. This prevents verification of soundness from the provided text and makes it impossible to assess whether the reported calibration improvements are statistically reliable or sensitive to evaluation choices.
minor comments (2)
  1. [Methods] Notation for prompt variants and confidence verbalization formats should be standardized in a table for clarity.
  2. [Results figures] Figure captions could more explicitly link plotted calibration curves to the specific prompt methods and datasets used.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for major revision. We address each major comment below with clarifications and proposed revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract; evaluation sections (likely §4–5)] The central claim that certain prompt methods produce well-calibrated verbalized scores rests on results from standard benchmarks (QA, classification, reasoning). These datasets may under-represent the ambiguity, distribution shift, and multi-step dependencies typical of downstream LLM-agent applications; without explicit tests on such tasks, the observed calibration may not transfer (see skeptic concern on benchmark representativeness).

    Authors: We agree that the evaluated benchmarks (primarily QA and classification tasks) do not fully capture the ambiguity, distribution shifts, or multi-step dependencies common in LLM-agent applications. Our work establishes that certain prompting strategies can yield well-calibrated verbalized scores on these standard tasks as a controlled baseline. We will add a limitations paragraph in the discussion section explicitly noting this scope and recommending future evaluations on agentic tasks to assess transfer. revision: partial

  2. Referee: [Abstract] Abstract states an 'extensive benchmark' but provides no details on calibration metrics (e.g., ECE definition), statistical significance tests, or data exclusion rules. This prevents verification of soundness from the provided text and makes it impossible to assess whether the reported calibration improvements are statistically reliable or sensitive to evaluation choices.

    Authors: The abstract is high-level by design, with full details on metrics (ECE defined and computed per Section 3), evaluation procedures, and data handling provided in the methods and results sections. To improve accessibility, we will revise the abstract to briefly name the primary metric (Expected Calibration Error) and direct readers to the relevant sections for definitions, statistical considerations, and exclusion criteria. revision: yes

Circularity Check

0 steps flagged

No derivation chain present; purely empirical evaluation

full rationale

The paper reports results from an extensive benchmark study comparing verbalized confidence scores under different prompt methods, models, and datasets. No mathematical derivations, fitted parameters, or load-bearing self-citations are used to establish the central claim. All reported outcomes are direct empirical measurements (e.g., calibration metrics on held-out benchmarks) that do not reduce to quantities defined inside the paper itself. The evaluation is therefore self-contained against external data.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are introduced; the work relies on standard empirical benchmarking practices in machine learning.

pith-pipeline@v0.9.0 · 5731 in / 849 out tokens · 31972 ms · 2026-05-23T06:34:42.432076+00:00 · methodology

0 comments
read the original abstract

The rise of large language models (LLMs) and their tight integration into our daily life make it essential to dedicate efforts towards their trustworthiness. Uncertainty quantification for LLMs can establish more human trust into their responses, but also allows LLM agents to make more informed decisions based on each other's uncertainty. To estimate the uncertainty in a response, internal token logits, task-specific proxy models, or sampling of multiple responses are commonly used. This work focuses on asking the LLM itself to verbalize its uncertainty with a confidence score as part of its output tokens, which is a promising way for prompt- and model-agnostic uncertainty quantification with low overhead. Using an extensive benchmark, we assess the reliability of verbalized confidence scores with respect to different datasets, models, and prompt methods. Our results reveal that the reliability of these scores strongly depends on how the model is asked, but also that it is possible to extract well-calibrated confidence scores with certain prompt methods. We argue that verbalized confidence scores can become a simple but effective and versatile uncertainty quantification method in the future. Our code is available at https://github.com/danielyxyang/llm-verbalized-uq.

Figures

Figures reproduced from arXiv: 2412.14737 by Daniel Yang, Makoto Yamada, Yao-Hung Hubert Tsai.

Figure 1
Figure 1. Figure 1: Uncertainty quantification for LLMs. Existing methods usually quantify the uncertainty based on the consistency of multiple sampled responses (Kuhn et al., 2022; Lin et al., 2023; Manakul et al., 2023; Tanneru et al., 2023; Xiong et al., 2023) or the internal token logits (Kadavath et al., 2022; Si et al., 2022; Ye et al., 2024). These approaches essentially let the LLM to self-assess its uncertainty based… view at source ↗
Figure 2
Figure 2. Figure 2: Different uncertainty quantification methods for LLMs. [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 6
Figure 6. Figure 6: Relative number of valid responses over all datasets per model and prompt method. The [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Calibration diagram for gemma1.1-2b. The color intensity of each bar is proportional to the bin size on a log scale. Note that the accuracy is close to uniform no matter on which range of confidence scores is conditioned. B.4 Insights into prompt methods 0.0 0.2 0.4 0.6 0.8 1.0 0.64 0.64 0.64 0.64 0.95 0.96 0.90 0.89 0.31 0.33 0.26 0.26 agg. over datasets[all], models[tiny] 1 basic basic_scorefloat basic_s… view at source ↗

discussion (0)

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Forward citations

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Reference graph

Works this paper leans on

2 extracted references · 2 canonical work pages · cited by 23 Pith papers · 2 internal anchors

  1. [1]

    The Falcon Series of Open Language Models

    AI@Meta (2024). Llama 3 Model Card. URL: https://github.com/meta- llama/llama3/ blob/main/MODEL_CARD.md, visited on 09/17/2024. Almazrouei, E., Alobeidli, H., Alshamsi, A., Cappelli, A., Cojocaru, R., Debbah, M., et al. (2023). The Falcon Series of Open Language Models. arXiv: 2311.16867. Bai, J., Bai, S., Chu, Y ., Cui, Z., Dang, K., Deng, X., et al. (20...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  2. [2]

    Language Models (Mostly) Know What They Know

    Kadavath, S., Conerly, T., Askell, A., Henighan, T., Drain, D., Perez, E., et al. (2022). Language Models (Mostly) Know What They Know. arXiv: 2207.05221. Klingbeil, A., Grützner, C., and Schreck, P. (2024). Trust and Reliance on AI — An Experimental Study on the Extent and Costs of Overreliance on AI. Computers in Human Behavior 160, page 108352. Kuhn, L...

    work page internal anchor Pith review Pith/arXiv arXiv 2022