October 8, 2025Open Access

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

View Full Paper

Ask AI

Bookmark

Authors

NNNariman Naderi ZAZahra Atf PLPeter Lewis

Discussion

Member takes

Overview

This analysis reveals how different prompt engineering techniques affect accuracy and confidence in medical contexts, highlighting the challenge of calibration.

Key Points

Chain-of-Thought prompts improved accuracy but increased overconfidence, leading to potential misjudgments in medical settings.
Five large language models were evaluated across 156 configurations, utilizing various prompt styles and confidence scales, revealing the need for careful calibration.
Calibrating confidence is essential, as emotional prompts inflating confidence can risk poor decisions in high-stakes medical tasks.
Smaller models like Llama-3.1-8b consistently underperformed, while proprietary models showed higher accuracy but still needed calibrated confidence.

Cite This Study

Naderi et al. (2025) studied this question.

synapsesocial.com/papers/68e6bc5f38ca8e474d549e8b https://doi.org/10.48550/arxiv.2506.00072

View Full Paper

Ask AI

Bookmark

October 8, 2025Open Access

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs

View Full Paper

Ask AI

Bookmark

Authors

NNNariman Naderi ZAZahra Atf PLPeter Lewis

Discussion

Member takes

Overview

This analysis reveals how different prompt engineering techniques affect accuracy and confidence in medical contexts, highlighting the challenge of calibration.

Key Points

Chain-of-Thought prompts improved accuracy but increased overconfidence, leading to potential misjudgments in medical settings.
Five large language models were evaluated across 156 configurations, utilizing various prompt styles and confidence scales, revealing the need for careful calibration.
Calibrating confidence is essential, as emotional prompts inflating confidence can risk poor decisions in high-stakes medical tasks.
Smaller models like Llama-3.1-8b consistently underperformed, while proprietary models showed higher accuracy but still needed calibrated confidence.

Cite This Study

Naderi et al. (2025) studied this question.

synapsesocial.com/papers/68e6bc5f38ca8e474d549e8b https://doi.org/10.48550/arxiv.2506.00072

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark