Paper Citation Record · LEDGER

Unleashing the potential of prompt engineering for large language models

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 34 inbound Pith citation observations for arXiv:2310.14735.

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

pith.paper-citation-record.v1
2310.14735 v6

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 34 of 34 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 34 of 34 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:14:06.058435Z

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

136
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 19b06830-51de-496a-917f-2a0e2c7ae7fc · inbound

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications cites this paper.

A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications Unleashing the potential of prompt engineering for large language models

Reference 3

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T21:52:10.375411Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 9705c63d-b736-4f49-bbc7-4be106d7d564 · inbound

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models cites this paper.

Sora: A Review on Background, Technology, Limitations, and Opportunities of Large Vision Models Unleashing the potential of prompt engineering for large language models

Reference 85

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T13:43:11.165248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4e83ed88-ff41-48b5-9f9a-056d927d5932 · inbound

Automated Design of Agentic Systems cites this paper.

Automated Design of Agentic Systems Unleashing the potential of prompt engineering for large language models

Reference 137

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T08:07:54.868652Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 57002682-87b1-4770-8bd3-31bbb899b940 · inbound

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications cites this paper.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications Unleashing the potential of prompt engineering for large language models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T15:14:06.058435Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 503eeb86-e807-46e4-992b-6a795876a6bc · inbound

Extracting Research Instruments from Educational Literature Using LLMs cites this paper.

Extracting Research Instruments from Educational Literature Using LLMs Unleashing the potential of prompt engineering for large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-07T13:23:55.390279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b59125f8-e08a-417b-a97e-b4bf184fc76f · inbound

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models cites this paper.

Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-07T13:14:02.046420Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d6da9cbe-cc77-4f30-b511-e0fead7168dc · inbound

From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs cites this paper.

From Parameters to Prompts: Understanding and Mitigating the Factuality Gap between Fine-Tuned LLMs Unleashing the potential of prompt engineering for large language models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T12:51:48.708313Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 77384217-9ebf-479b-bbe8-7cfe0216e285 · inbound

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation cites this paper.

Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation Unleashing the potential of prompt engineering for large language models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-07T05:42:43.542405Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5879a405-2825-4be3-b9e4-0239d0a603b3 · inbound

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations cites this paper.

FAA Framework: A Large Language Model-Based Approach for Credit Card Fraud Investigations Unleashing the potential of prompt engineering for large language models

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-07T04:08:12.838692Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 64aeb19d-f950-4a12-80f5-ecdb0817f40c · inbound

Designing Effective LLM-Assisted Interfaces for Curriculum Development cites this paper.

Designing Effective LLM-Assisted Interfaces for Curriculum Development Unleashing the potential of prompt engineering for large language models

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:12.966176Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37e1955a-6b64-448a-ac7d-5299f0386ba9 · inbound

AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns cites this paper.

AI-Facilitated Analysis of Abstracts and Conclusions: Flagging Unsubstantiated Claims and Ambiguous Pronouns Unleashing the potential of prompt engineering for large language models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T00:40:00.360748Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 467dc43d-428d-4010-b112-11a64ed6866d · inbound

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation cites this paper.

Can GPT-4o Evaluate Usability Like Human Experts? A Comparative Study on Issue Identification in Heuristic Evaluation Unleashing the potential of prompt engineering for large language models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:52:13.035552Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 68dec1d7-e9d3-4631-b487-2ffd602b4a66 · inbound

Evaluating and Improving Large Language Models for Competitive Program Generation cites this paper.

Evaluating and Improving Large Language Models for Competitive Program Generation Unleashing the potential of prompt engineering for large language models

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-06T22:03:30.526162Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 33c126e4-8dd4-4bd2-ba23-f94b56f25f46 · inbound

Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models cites this paper.

Enhancing COBOL Code Explanations: A Multi-Agents Approach Using Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-06T20:40:00.584492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a76b10b2-12e3-456b-9b59-3698866d0811 · inbound

LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction cites this paper.

LLM-based Question-Answer Framework for Sensor-driven HVAC System Interaction Unleashing the potential of prompt engineering for large language models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:43:56.084903Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fb319519-47b3-41bf-bf55-c113173c43b7 · inbound

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques cites this paper.

An Evaluation of Large Language Models on Text Summarization Tasks Using Prompt Engineering Techniques Unleashing the potential of prompt engineering for large language models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T19:37:18.221170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c8c796cc-e5f8-4a47-b86e-e6ecee701dcf · 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 Unleashing the potential of prompt engineering for large language models

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-06T18:35:40.882361Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 849ddd32-fa9e-479d-9be5-a641bbab57a9 · inbound

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models cites this paper.

Promptomatix: An Automatic Prompt Optimization Framework for Large Language Models Unleashing the potential of prompt engineering for large language models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-06T16:30:58.612262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 068e527a-ee63-4e67-a3ba-ceccb67c9894 · inbound

CaTE Data Curation for Trustworthy AI cites this paper.

CaTE Data Curation for Trustworthy AI Unleashing the potential of prompt engineering for large language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-05T18:23:21.760543Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 864208de-1f96-4095-854d-3d27169f9c6e · inbound

CaTE Data Curation for Trustworthy AI cites this paper.

CaTE Data Curation for Trustworthy AI Unleashing the potential of prompt engineering for large language models

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T18:23:21.858371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9ab8cb29-450f-4460-9d6e-759f20f9b023 · inbound

Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis cites this paper.

Investigation of the Inter-Rater Reliability between Large Language Models and Human Raters in Qualitative Analysis Unleashing the potential of prompt engineering for large language models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T18:22:38.853411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f341147c-ce98-4819-b3f0-08e6ef2fdb7f · inbound

Using an LLM to Investigate Students' Explanations on Conceptual Physics Questions cites this paper.

Using an LLM to Investigate Students' Explanations on Conceptual Physics Questions Unleashing the potential of prompt engineering for large language models

Reference 30

Resolution: verified exact; arxiv_id, observed 2026-05-18T22:06:52.022419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 1677dc1e-aa1b-44ae-995d-32f8676a436b · inbound

Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia cites this paper.

Using LLMs to create analytical datasets: A case study of reconstructing the historical memory of Colombia Unleashing the potential of prompt engineering for large language models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T11:02:53.339463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 42249d02-f410-4c20-a47f-dca93dd21728 · inbound

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial cites this paper.

Large Language Models for Next-Generation Wireless Network Management: A Survey and Tutorial Unleashing the potential of prompt engineering for large language models

Reference 171

Resolution: unresolved; no resolver link, observed 2026-08-05T04:50:32.154805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 050c9420-fb39-4d51-8d9a-30a7235816ad · inbound

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data cites this paper.

PIAST: Rapid Prompting with In-context Augmentation for Scarce Training data Unleashing the potential of prompt engineering for large language models

Reference 23

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:18:40.058929Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation f7ab7f8a-1aa9-4df0-9ad8-c4041a6dd421 · inbound

Prompts Blend Requirements and Solutions: From Intent to Implementation cites this paper.

Prompts Blend Requirements and Solutions: From Intent to Implementation Unleashing the potential of prompt engineering for large language models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T18:07:18.954923Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 622a3c62-0e37-4e0d-a139-e17790836931 · inbound

Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions cites this paper.

Benchmarking LLM-Based Static Analysis for Secure Smart Contract Development: Reliability, Limitations, and Potential Hybrid Solutions Unleashing the potential of prompt engineering for large language models

Reference 7

Resolution: verified exact; arxiv_id, observed 2026-05-13T02:22:06.988579Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation b58ea4d6-4122-4ced-b4da-1c267018971b · inbound

PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection cites this paper.

PromptAudit: Auditing Prompt Sensitivity in LLM-Based Vulnerability Detection Unleashing the potential of prompt engineering for large language models

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:44:56.332416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 474b604f-8ab2-42b7-9976-5d1dc725f0d3 · inbound

Enhancing Reliability in LLM-Based Secure Code Generation cites this paper.

Enhancing Reliability in LLM-Based Secure Code Generation Unleashing the potential of prompt engineering for large language models

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-06-30T15:14:46.058532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 3a8c28ac-c9c1-4d72-907d-2e3d69f02045 · inbound

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems cites this paper.

IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems Unleashing the potential of prompt engineering for large language models

Reference 55

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T23:26:22.001615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 4d07c1b1-57d4-4e49-9e23-91428cc80578 · inbound

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG cites this paper.

Wait, am I Being Fair? Characterizing Deductive Stereotyping and Mitigating It with Fair-GCG Unleashing the potential of prompt engineering for large language models

Reference 77

Resolution: verified exact; arxiv_id, observed 2026-07-01T01:15:13.264009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2316d6d9-0949-4fd3-a476-0dc9f07ec03a · inbound

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs cites this paper.

Prompt engineering using order-of-addition experiments: An application to generating two-level fractional factorial designs Unleashing the potential of prompt engineering for large language models

Reference 63

Resolution: unresolved; no resolver link, observed 2026-07-11T06:09:35.110633Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 667f6769-3337-4e71-96be-ae554a9f108e · inbound

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies cites this paper.

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies Unleashing the potential of prompt engineering for large language models

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T14:54:47.251635Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70b3a75a-4a82-4920-961d-9423d7ef6637 · inbound

Using LLMs to Detect Growth in Computational Thinking in Introductory Physics cites this paper.

Using LLMs to Detect Growth in Computational Thinking in Introductory Physics Unleashing the potential of prompt engineering for large language models

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-07T12:48:59.677280Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.