Paper Citation Record · LEDGER

Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2304.13712.

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

pith.paper-citation-record.v1
2304.13712 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T04:28:00.896849Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T17:47:17.806699Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation d25f4ad0-005d-4fdc-88b0-cc0173297700 · inbound

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 75

Resolution: verified exact; arxiv_id, observed 2026-05-17T18:00:50.172211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30740c29-bdfd-475d-8214-cd9e36594dcc · inbound

A Survey on the Memory Mechanism of Large Language Model based Agents cites this paper.

A Survey on the Memory Mechanism of Large Language Model based Agents Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-15T07:21:39.780328Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 63735695-4d7e-421b-a737-886878911384 · inbound

Selective Shot Learning for Code Explanation cites this paper.

Selective Shot Learning for Code Explanation Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-11T13:42:48.551161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a975bec0-5640-4fbe-9c96-c339c6298b0e · inbound

Adaptations of AI models for querying the LandMatrix database in natural language cites this paper.

Adaptations of AI models for querying the LandMatrix database in natural language Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-11T13:36:21.251862Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2929ab7b-5a14-4bed-8c8e-7c8407669edb · inbound

Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy cites this paper.

Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T05:29:38.289100Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5794cec3-8134-48a1-9fce-9f8d4c7fd763 · inbound

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures cites this paper.

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 159

Resolution: unresolved; no resolver link, observed 2026-08-16T04:28:00.896849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d39344c-540b-4b2a-a581-5ed764e32be0 · inbound

Origin Tracer: A Method for Detecting LoRA Fine-Tuning Origins in LLMs cites this paper.

Origin Tracer: A Method for Detecting LoRA Fine-Tuning Origins in LLMs Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T14:17:22.212190Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87d00a8b-bf6f-4ec7-b302-d550b6329b05 · inbound

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization cites this paper.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:45.245495Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0e9443b-1feb-4be5-ae4f-10755cb26773 · inbound

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion cites this paper.

FedProxy: Federated Fine-Tuning of LLMs via Proxy SLMs and Heterogeneity-Aware Fusion Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 27

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T12:56:05.890823Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7f3ca5fe-bae2-4a25-a699-c05f2a94db33 · inbound

Empirical Evaluation of Large Language Models for Migration of Code Fragments to Post-Quantum Cryptography cites this paper.

Empirical Evaluation of Large Language Models for Migration of Code Fragments to Post-Quantum Cryptography Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond

Reference 48

Resolution: verified exact; arxiv_id, observed 2026-07-02T17:47:17.808163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.