REVIEW 4 major objections 4 minor 20 references
Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs
T0 review · 4 major / 4 minor · reviewed 2026-08-07 · deepseek-v4-flash
Pith's one-line read This paper argues that prompt engineering for medical LLMs must be judged on both accuracy and confidence calibration, because reasoning prompts that raise accuracy also inflate overconfidence and emotional prompts inflate confidence…
desk verdict A useful Persian medical prompt-engineering benchmark, but the comparative claims need statistical grounding before they can be trusted. read the letter →
The pith
A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.
The reading
What carries the argument
The argument is carried by an experimental grid: 300 stratified Persian board certification questions, five LLMs of different sizes and vendors, six prompt styles (Chain-of-Thought, Few-Shot, Hybrid, Zero-Shot Raw, Expert Mimicry, Emotional), three temperatures, and two confidence scales, producing 156 configurations. Each run is forced through a structured output that separates reasoning, a final answer from four options, and a numeric confidence score. The confidence score is then treated as a predicted probability and scored with calibration metrics (Brier score, Expected Calibration Error) and discrimination metrics (AUC-ROC), so the central mechanism is the comparison between what the model says about its own certainty and whether the answer is actually correct.
What would settle it
A reader could settle the claim by re-scoring the same model outputs after normalizing each confidence value by its scale maximum and applying a standard calibration mapping; if any configuration then achieved a Brier score below 0.25 or an AUC-ROC above 0.7, the paper's conclusion that no tested setting is well calibrated would not survive. Equally, collecting open-ended verbal confidence or self-consistency-based uncertainty on the same 300 questions and showing good calibration would contradict the claim that current confidence elicitation methods are unreliable.
Extended reading notes
Core claim
The paper's central claim is that, in medical question answering, the way a prompt asks for an answer changes both what the model gets right and how confident it says it is, and these two effects can pull in opposite directions. Specifically, the authors claim that Chain-of-Thought prompting improves accuracy across architectures but systematically inflates self-assessed confidence; emotional prompting inflates confidence further while failing to improve accuracy over a zero-shot baseline; expert mimicry improves accuracy but also increases overconfidence; and finer 1-100 confidence scales do not yield better calibration than 1-10 scales. Across all 156 experimental settings, no model reached a Brier score below 0.25 or an AUC-ROC above 0.7, which the paper reads as evidence that current confidence elicitation methods are not reliable for high-stakes medical use.
Load-bearing premise
The load-bearing assumption is that a model's self-reported number on a 1-10 or 1-100 scale can be treated as a linear predicted probability when computing Brier score and ECE; if those numbers are arbitrary text rather than calibrated probabilities, the overconfidence results may be artifacts of the elicitation format.
Editorial extensions
If this is right
- Chain-of-Thought prompting should not be deployed in clinical decision support without a post-hoc calibration step, because its accuracy gains come with inflated confidence.
- Emotional framing should be avoided in medical question prompts: it raises confidence without improving accuracy and degrades both calibration and discrimination.
- Verbalized confidence on a 1-10 or 1-100 scale should not be read as a calibrated probability; no tested configuration met basic calibration thresholds.
- Smaller models such as Llama-3.1-8B are unsuitable for high-stakes medical use by these results, underperforming on accuracy and calibration alike.
- Model choice matters more than prompt style for calibration: GPT-4o had the best mean Brier score despite lower accuracy than o3-mini.
Reading between the lines
- A natural extension would be to test whether rescaling or post-hoc binning of the reported confidence values (for example, dividing by the scale maximum and applying a calibration mapping) moves Brier scores below 0.25; the paper does not report such a normalization.
- The finding that emotional prompts inflate confidence suggests that affectively framed user interfaces around medical AI may induce similar over-trust in clinicians, a hypothesis the paper does not test directly.
- Because confidence was elicited as a single number in a structured output, the results leave open whether open-ended verbalized uncertainty or sampling-based consistency measures would show better calibration.
Editorial analysis
A structured set of objections, weighed in public.
Referee Report
Summary. The paper reports a large empirical study of prompt engineering effects on accuracy and confidence elicitation in medical question answering. Using a stratified sample of 300 Persian board exam questions, the authors evaluate five LLMs (GPT-4o, o3-mini, Llama-3.3-70b, Llama-3.1-8b, DeepSeek-v3) under 156 configurations that vary temperature, prompt style (Chain-of-Thought, Few-Shot, Hybrid, Zero-Shot, Expert Mimicry, Emotional), and confidence scale (1–10, 1–100). The headline findings are that Chain-of-Thought prompting improves accuracy but inflates confidence, emotional prompting further inflates confidence, and no configuration achieves well-calibrated confidence as measured by Brier score, ECE, or AUC-ROC. The paper concludes that prompt engineering must be paired with post-hoc calibration for trustworthy medical LLM use.
Significance. If the findings are supported, the paper would provide a practically relevant warning about the accuracy/calibration trade-off in medical LLMs and a substantial comparative benchmark across models, prompts, and temperatures. The systematic coverage of 156 configurations, the use of externally validated ground truth, and the availability of a GitHub repository are strengths. However, the quantitative support is weakened by a lack of inferential statistics and by incomplete documentation of the calibration metrics. The headline ordering of prompt techniques (CoT improves accuracy, emotional prompting inflates confidence, no model is well calibrated) is plausible but not yet established from the reported numbers.
major comments (4)
- [§4.1, §5] The paper draws comparative conclusions from single-run results for each of 156 configurations, but no statistical test, confidence interval, or variance estimate is reported. In §5, the authors state that emotional prompting 'showed no statistically significant improvement over zero-shot baselines,' yet no significance test appears anywhere in the manuscript. For a single run of 300 items, the standard error of an accuracy proportion near 0.67 is roughly 2.7 percentage points, and the standard error of a difference between two configurations is roughly 4 points. The §4.1 example of o3-mini emotional (69.00%) versus zero-shot (67.67%) is therefore within sampling noise. The load-bearing claims about prompt ordering and about 'consistent' effects lack the statistical support needed to be established.
- [§3.5] The Brier score and ECE are said to be computed from 'predicted probabilities (i.e., confidence values),' but the confidence values are elicited on 1–10 or 1–100 scales. The manuscript never states how these values are rescaled to probabilities in [0,1], nor how ECE bins are defined. The reported Brier values near 0.25–0.6 (e.g., §4.3) are only plausible after such normalization, but without an explicit formula the calibration results are not reproducible and comparisons across the two scales are undefined. This is a load-bearing gap because the paper's central conclusion of systemic overconfidence rests on these metrics.
- [§5] The claim that 'No model achieved a Brier score below 0.25 (commonly approximating random-chance performance in a four-option setting)' is misleading. If confidence is treated as the probability of the chosen answer, random chance in a four-option question corresponds to p=0.25, which gives an expected Brier score of 0.1875, not 0.25; the value 0.25 corresponds to p=0.5. The stated threshold therefore does not support the 'systemic overconfidence' interpretation without a clearly defined reference model. This issue should be corrected and the claim re-evaluated against a proper random baseline.
- [§4.1, §4.2] The aggregated '±' values (e.g., '66.36 ± 0.70%' for o3-mini in §4.1 and 'mean AUC-ROC of 0.627±0.01' in §4.2) are presented without defining what the interval represents. If these are standard deviations across configurations, they convey dispersion over prompt/temperature settings, not sampling uncertainty for a single configuration. The use of these values in the Discussion to compare models implicitly treats them as if they were confidence intervals, which would be misleading. The authors should specify the quantity and report proper bootstrap or analytic confidence intervals for the key comparisons.
minor comments (4)
- [§2 (References)] Reference [9] is used for two different works in the Related Work section: Yang et al. (rephrased queries) and Yadkori et al. (iterative prompting), although Yadkori et al. is listed as reference [6]. This citation error should be corrected throughout.
- [§3.2, Figures 2 and 3] The text states that o3-mini only supports temperature 1.0, but the heatmaps in Figures 2 and 3 appear to include cells for o3-mini at temperatures 0.3 and 0.7. Please clarify which configurations were actually run and ensure the figures match the text.
- [§1.1] The contribution statement mentions evaluating 'direct and indirect queries,' but the methodology only describes six prompt styles (CoT, Few-Shot, CoT+Few-Shot, Zero-Shot, Expert Mimicry, Emotional). The inconsistent phrase should be removed or the missing prompt styles should be added.
- [Throughout] There are multiple typographical errors, including 'Figer 2' in §3.1, 'gpt-4owith' in §4.1, 'Asencio' in §3.6, and 'chai n of thought' in the captions of Figures 2 and 3. A careful proofreading pass is needed.
Circularity Check
No circularity: every reported metric compares observed model outputs to an external ground-truth answer key; the only self-citations are peripheral to the empirical claims.
full rationale
This paper is an empirical evaluation rather than a derivation chain. Accuracy is scored against an externally verified Persian board-exam answer key; confidence scores are model-generated tokens elicited under fixed prompt templates; and calibration metrics (Brier, ECE, AUC-ROC) are computed from the joint distribution of stated confidence and correctness. No parameter is fitted to the outcome and then renamed as a prediction; no equation defines the independent variables in terms of the dependent variables; and no uniqueness theorem or prior result by the authors is invoked to force the conclusions. The only self-citations (Refs. 18 and 19) appear in the philosophical discussion of reflective AI and are not load-bearing for any empirical result. Treating 1-10 and 1-100 confidence values as probabilities for Brier/ECE is a validity assumption and a reporting gap, but it is not circular: the values are observed outputs, not quantities constructed from the target conclusion. Similarly, the absence of significance testing or variance estimation is a statistical-underdetermination concern, not a circularity concern. Therefore the circularity score is 0.
Assumptions & free parameters
assumptions (3)
- domain assumption Persian board exam accuracy is a valid proxy for clinical decision-making competence.
- domain assumption Self-reported confidence values on 1-10 and 1-100 scales can be treated as probabilities for Brier and ECE calculation.
- domain assumption The models had not memorized the 2022-2023 Persian board questions during pretraining.
Cite this review
Pith. "Pith review of Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs." pith.science (2026). https://pith.science/paper/RDBHOTOZ
@misc{pith2026250600072,
author = {Pith},
title = {Pith review of: Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs},
year = {2026},
howpublished = {\url{https://pith.science/paper/RDBHOTOZ}},
note = {Machine review of arXiv:2506.00072}
}
read the original abstract
This paper investigates how prompt engineering techniques impact both accuracy and confidence elicitation in Large Language Models (LLMs) applied to medical contexts. Using a stratified dataset of Persian board exam questions across multiple specialties, we evaluated five LLMs - GPT-4o, o3-mini, Llama-3.3-70b, Llama-3.1-8b, and DeepSeek-v3 - across 156 configurations. These configurations varied in temperature settings (0.3, 0.7, 1.0), prompt styles (Chain-of-Thought, Few-Shot, Emotional, Expert Mimicry), and confidence scales (1-10, 1-100). We used AUC-ROC, Brier Score, and Expected Calibration Error (ECE) to evaluate alignment between confidence and actual performance. Chain-of-Thought prompts improved accuracy but also led to overconfidence, highlighting the need for calibration. Emotional prompting further inflated confidence, risking poor decisions. Smaller models like Llama-3.1-8b underperformed across all metrics, while proprietary models showed higher accuracy but still lacked calibrated confidence. These results suggest prompt engineering must address both accuracy and uncertainty to be effective in high-stakes medical tasks.
Reference graph
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Reviewed August 7, 2026 · model on record in the stance chip above.
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