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Paper Citation Record · LEDGER

Improving ChatGPT Prompt for Code Generation

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.08360.

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

pith.paper-citation-record.v1
2305.08360 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T14:00:07.854136Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-13T20:13:13.121796Z

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 b37d11f7-f392-48ef-bbb3-fd1e41403a8e · inbound

Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering cites this paper.

Instruct or Interact? Exploring and Eliciting LLMs' Capability in Code Snippet Adaptation Through Prompt Engineering Improving ChatGPT Prompt for Code Generation

Reference 54

Resolution
unresolved
no resolver link, observed 2026-08-12T14:18:31.460595Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T14:18:31.460595Z digest=sha256:874329692faeb925c075158842afce30d39939d51f71d3341611d5290bb1f6ff

Observation ffb9864a-7a23-416c-8ac7-9ee9e2a30865 · inbound

SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework cites this paper.

SmartLLMSentry: A Comprehensive LLM Based Smart Contract Vulnerability Detection Framework Improving ChatGPT Prompt for Code Generation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-12T10:25:40.722024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T10:25:40.722024Z digest=sha256:b23c397b2c86d8357a1f4bb19dc9e9e05d6d6dad469babcf48542bdc32dc4d85

Observation c1efbb6c-cb1f-4514-96e8-86aa6f5b7732 · inbound

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code cites this paper.

SnipGen: A Mining Repository Framework for Evaluating LLMs for Code Improving ChatGPT Prompt for Code Generation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-08T14:01:12.416321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T14:01:12.416321Z digest=sha256:7a3966179a2f2005df701b488ba42ac2fa5206af0228aab089a06870042a7248

Observation c1c9f96f-c69d-4c65-8672-3755864e747c · inbound

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation cites this paper.

Evaluating the Environmental Impact of using SLMs and Prompt Engineering for Code Generation Improving ChatGPT Prompt for Code Generation

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:13:13.124436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-13T20:12:12.283350Z digest=sha256:f0579d8cfb26c2c1ed84843ef0eeca74c300dbeba3cfc7f9d13686b179862373

Observation a4f92694-45b7-48b8-b572-e44adcd93227 · inbound

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality cites this paper.

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality Improving ChatGPT Prompt for Code Generation

Reference 57

Resolution
unresolved
no resolver link, observed 2026-08-02T01:01:33.416614Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:01:33.416614Z digest=sha256:41a781b8e48f91b6e8b77f9944ec196ce6009975196b68f8986b645ac82e6f5b

Observation e5411c58-e075-481a-8c7f-39e76638f4e5 · inbound

Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation cites this paper.

Do Influence Tactics Matter? Investigating Prompt Framing Effects in LLM Code Generation Improving ChatGPT Prompt for Code Generation

Reference 37

Resolution
unresolved
no resolver link, observed 2026-08-15T14:00:07.854136Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T14:00:07.854136Z digest=sha256:52798c4c721eb0eb2b452b8cb2f0665c7d029f9573f43073b2b10491de928f38