Paper Citation Record · LEDGER

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

As of 18 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2506.00072.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2506.00072 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T12:44:15.178000Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact3
verified fuzzy4
unresolved13
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 321d4570-e7c0-4881-8276-0ef8610a301e · outbound

This paper cites Prompt engineering in consistency and reliability with the evidence -based guideline for LLMs,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Prompt engineering in consistency and reliability with the evidence -based guideline for LLMs,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:13.407078Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 11292c54-d12b-46cc-a6a2-f842c2fb2cc2 · outbound

This paper cites What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Exams

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:13.493701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 61cf2c04-a9ec-46f8-8751-a16c5bab99ca · outbound

This paper cites Language Models (Mostly) Know What They Know.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Language Models (Mostly) Know What They Know

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:13.558826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 07a4576b-f33d-498c-a98c-fff796ad2d6f · outbound

This paper cites Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:13.653907Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 732f8f56-37d2-48f7-be30-0647762929f5 · outbound

This paper cites Do Language Models Know When They’re Hallucinating References?.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Do Language Models Know When They’re Hallucinating References?

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:16.083415Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e9d771cd-bba3-4ecc-8608-541524ab3ee3 · outbound

This paper cites To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs To Believe or Not to Believe Your LLM: Iterative Prompting for Estimating Epistemic Uncertainty

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:15.897714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4f02dd20-f97d-4038-8f17-d0972a1d0d27 · outbound

This paper cites What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs What Did I Do Wrong? Quantifying LLMs' Sensitivity and Consistency to Prompt Engineering

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.027058Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8b311c8-fed6-4e90-923c-0aa4fe80bae4 · outbound

This paper cites Accuracy and Consistency of LLMs in the Registered Dietitian Exam: The Impact of Prompt Engineering and Knowledge Retrieval.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Accuracy and Consistency of LLMs in the Registered Dietitian Exam: The Impact of Prompt Engineering and Knowledge Retrieval

Reference 8

Resolution: verified exact; local_arxiv, observed 2026-08-07T12:44:15.555435Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 5b7c87c0-0b91-4d7c-8e50-625dbf891d66 · outbound

This paper cites Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Just rephrase it! Uncertainty estimation in closed-source language models via multiple rephrased queries

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.228184Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6672b5a3-c938-4ce4-9f87-be8c44f01a67 · outbound

This paper cites Cycles of Thought: Measuring LLM Confidence through Stable Explanations.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Cycles of Thought: Measuring LLM Confidence through Stable Explanations

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.335426Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9eeaba60-fdb7-4ed9-a534-dbf2b13407b5 · outbound

This paper cites Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effec- tively in A Self -Training Manner,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Can LLMs Learn Uncertainty on Their Own? Expressing Uncertainty Effec- tively in A Self -Training Manner,

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.489208Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b307add0-1b13-438a-b1b6-565509be2168 · outbound

This paper cites Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Bayesian Prompt Ensembles: Model Uncertainty Estimation for Black-Box Large Language Models,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.604595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c9240b8f-e237-4d3c-9072-3d7ac9019e90 · outbound

This paper cites Uncertainty Quantification for In -Context Learning of Large Language Models,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Uncertainty Quantification for In -Context Learning of Large Language Models,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:15.748927Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 6a9a8eb7-fd15-4794-8c89-37a8138a8f3d · outbound

This paper cites Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Examining the robustness of LLM evaluation to the distributional assumptions of benchmarks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.888885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6016f046-b748-46a3-b739-b406c6ab5052 · outbound

This paper cites SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs SaySelf: Teaching LLMs to Express Confidence with Self-Reflective Rationales

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.955084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a11d125-1d7e-4168-9967-ca9553b8f26d · outbound

This paper cites Large language model uncertainty proxies : discrimination and calibration for medical diagnosis and treatment,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Large language model uncertainty proxies : discrimination and calibration for medical diagnosis and treatment,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T12:44:15.678308Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation d451b0e4-02f1-4f76-b3b1-c428a4bca82d · outbound

This paper cites Harnessing Response Consistency for Superior LLM Performance: The Promise and Peril of Answer-Augmented Prompting,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Harnessing Response Consistency for Superior LLM Performance: The Promise and Peril of Answer-Augmented Prompting,

Reference 17

Resolution: verified exact; doi, observed 2026-08-07T12:44:15.405010Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8979d06b-76b3-4f2d-9112-bfd7ff0242d4 · outbound

This paper cites Reflective Artificial Intelligence,.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs Reflective Artificial Intelligence,

Reference 18

Resolution: verified exact; doi, observed 2026-08-07T12:44:15.250269Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation ddd012bd-f656-4762-ba50-88888c623c80 · outbound

This paper cites The challenge of uncertainty quantification of large language models in medicine.

Evaluating Prompt Engineering Techniques for Accuracy and Confidence Elicitation in Medical LLMs The challenge of uncertainty quantification of large language models in medicine

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:15.178000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f1a83321-9c02-4459-a842-d008a5eee9ff · outbound

This paper cites an unresolved cited work.

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

Reference 3370

Resolution: unresolved; no resolver link, observed 2026-08-07T12:44:14.806952Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.