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Paper Citation Record · LEDGER

ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2305.14688.

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

pith.paper-citation-record.v1
2305.14688 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T20:20:35.389730Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

53
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bbef2bdf-6061-415c-8f2e-aad6ef8f7581 · inbound

Dr. Jekyll and Mr. Hyde: Two Faces of LLMs cites this paper.

Dr. Jekyll and Mr. Hyde: Two Faces of LLMs ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-24T05:06:00.332067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-24T05:05:35.726118Z digest=sha256:ff016b8a8417f11e24e1d9969eb9d0a3c866b5b71154ef1aeeab675c3ccb0356

Observation 1499a65f-6eb8-440c-99bf-164549f1b6fd · inbound

Automated Design of Agentic Systems cites this paper.

Automated Design of Agentic Systems ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 224

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T08:07:54.854381Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-15T08:07:54.611771Z digest=sha256:82ab2b0eabea7a269b77d311b3b48c3aa9120aca01ac80d25c790c0b05b0c1d5

Observation c8e6a269-8973-48b8-9f09-b9ac176fc4f7 · inbound

RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models cites this paper.

RankFlow: A Multi-Role Collaborative Reranking Workflow Utilizing Large Language Models ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 68

Resolution
verified exact
arxiv_id, observed 2026-05-23T04:37:31.779033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T04:36:32.897387Z digest=sha256:586a88e937777cb4c9a11a7c0b13f5bd82910b764cd860945eb2009cf42161d5

Observation 2e481466-5886-4187-9bb8-399eadc7eee4 · inbound

LLMs Can Teach Themselves to Better Predict the Future cites this paper.

LLMs Can Teach Themselves to Better Predict the Future ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-08T20:20:35.389730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T20:20:35.389730Z digest=sha256:bbdaf472577e3e5a71020ca2da8c4860b731e1c987b73c7d291f9ca1072f56f7

Observation b76a2f3f-0b6a-43a4-aeea-ccf559f0616b · inbound

Teaching Astronomy with Large Language Models cites this paper.

Teaching Astronomy with Large Language Models ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 58

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:17:15.398297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T10:14:51.134936Z digest=sha256:daf720ac05b03d3e2ed09fc4e9964a3a831b2bdfb395c8ee7c5ef2d3d4415fe0

Observation 549ec450-da4f-4584-85ba-2e17cfc4c874 · inbound

Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture cites this paper.

Exploring Advanced LLM Multi-Agent Systems Based on Blackboard Architecture ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T20:51:15.174527Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T20:51:15.174527Z digest=sha256:caf023949ea5a35802f38900555ad5dafd90fd870fac6b3187c89a511e83b98f

Observation 1223bc3c-44b7-4706-b00c-e34cbdd8c8c0 · inbound

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis cites this paper.

An Integrated Framework of Prompt Engineering and Multidimensional Knowledge Graphs for Legal Dispute Analysis ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-06T18:35:40.890722Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:35:40.890722Z digest=sha256:f65b3d7f6602f3bd2719bab38e76a51df9adae9a5e3d938aaa4988e9c664bf0f

Observation 6a0151d1-5578-4879-8c21-4cc38d70bb5b · inbound

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs cites this paper.

SLIP: Soft Label Mechanism and Key-Extraction-Guided CoT-based Defense Against Instruction Backdoor in APIs ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 3

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T00:56:56.348748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-19T00:54:00.464780Z digest=sha256:733187454dca6b8d6c88fbf1283bff37dd9abbade67f315e42b76e021a48dc64

Observation a408db24-e1ef-43db-82be-8c80c94f4451 · inbound

Integrating gender inclusivity into large language models via instruction tuning cites this paper.

Integrating gender inclusivity into large language models via instruction tuning ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-05T16:31:10.080720Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-05T16:31:10.080720Z digest=sha256:c3abe1bbdbfd0e1de38f091f67fbd1d56372b64f22f1d4ade031c7c693a7efda

Observation a817f4bf-55e7-41a7-b0c3-e5941a825854 · inbound

XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad cites this paper.

XCR-Bench: Benchmarking Cross-Cultural Reasoning in LLMs via Culture-Specific Items and Hall's Triad ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-03T09:22:26.051080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T09:22:26.051080Z digest=sha256:a3de32a11739948861d623fd5ec1f4fe1ef3e1d0afac7ddfd5f05c6035155d44

Observation 491b1b2d-3752-44b2-908d-2f4af0aeaf2c · inbound

Understanding the Mechanism of Altruism in Large Language Models cites this paper.

Understanding the Mechanism of Altruism in Large Language Models ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 213

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:31:02.305454Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-10T01:36:50.329664Z digest=sha256:4ec6cbb95769e16d28efeb4cb7ffb4966c9994aab386015a2dbfebe476d52c16

Observation 7d3e10a7-d49b-4d0d-a612-23400569416d · inbound

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment cites this paper.

The Moltbook Files: A Harmless Slopocalypse or Humanity's Last Experiment ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-11T01:55:50.750377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-05-11T01:54:49.131461Z digest=sha256:a0f28fee01969f137cbe7f82bdcd5898a346959127f11bf97c86c4d7a5e88362

Observation fa6aad0a-61ab-4410-b38f-73cac9f0c58e · inbound

Using Large Language Models in Physics Education cites this paper.

Using Large Language Models in Physics Education ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-05-25T02:25:13.850785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-25T02:24:48.461128Z digest=sha256:44429e32f02fee3c39a1ca47eed91219c767cff64114ae3b83c325fc38404e32

Observation 1c382493-3117-42de-91dc-1db427f4e167 · inbound

Using Large Language Models in Physics Education cites this paper.

Using Large Language Models in Physics Education ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 41

Resolution
verified exact
arxiv_id, observed 2026-06-30T15:04:46.062221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-06-30T14:58:55.227580Z digest=sha256:6e105aac8b496238beb24a5ab061279405365ae11d934cba91c00cf7205fe3af

Observation f6183354-4edb-4243-a9e9-c15471137892 · inbound

Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs cites this paper.

Label Over Logic? How Source Cues Bias Human Fallacy Judgments More Than LLMs ExpertPrompting: Instructing Large Language Models to be Distinguished Experts

Reference 65

Resolution
verified exact
arxiv_id, observed 2026-06-29T05:43:08.665733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=arxiv_source observed=2026-06-29T05:36:21.058156Z digest=sha256:66a6ef4bef1ddab2eb3bd44d261eba37200b2d15daa01ee67ad86c3b1027753b