Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2310.10508.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:36.325336Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-04T01:49:21.977472Z
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 196a7a00-9828-4bd2-b380-a351a5b55f35 · inbound
CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology cites this paper.
CodePori: Large-Scale System for Autonomous Software Development Using Multi-Agent Technology Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 58
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T03:58:51.223784Z
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.
Observation 6d3e0532-4d82-42b1-9006-bfcc5a769866 · inbound
Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation cites this paper.
Resilient LLM-Empowered Semantic MAC Protocols via Zero-Shot Adaptation and Knowledge Distillation Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:09:36.325336Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b83dc8b9-f798-4924-98a5-c40550c8ecbb · inbound
Mobile Application Review Summarization using Chain of Density Prompting cites this paper.
Mobile Application Review Summarization using Chain of Density Prompting Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 16
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:23:28.686146Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation aa3d31b5-84b5-4a89-8e51-3b6b347c02c2 · inbound
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models cites this paper.
Efficient Black-Box Fault Localization for System-Level Test Code Using Large Language Models Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T07:32:08.685790Z
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.
Observation 37ba835b-a86c-49ba-87c7-e1b1a02f7947 · inbound
A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights cites this paper.
A Pilot Study on LLM-Based Agentic Translation from Android to iOS: Pitfalls and Insights Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:24:10.695958Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a41307d4-cdb9-4d73-807c-a83d07fe959b · inbound
Extension Decisions in Open Source Software Ecosystem cites this paper.
Extension Decisions in Open Source Software Ecosystem Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 100
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T11:05:32.621605Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation b2e5c741-c7b8-4b93-a617-2ac3a0dfbdea · inbound
Prompt-Driven Code Summarization: A Systematic Literature Review cites this paper.
Prompt-Driven Code Summarization: A Systematic Literature Review Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 95
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T11:35:18.559436Z
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.
Observation e1975792-d0b9-4590-bcdd-0a888bcfffe2 · inbound
OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms cites this paper.
OMEGA: Optimizing Machine Learning by Evaluating Generated Algorithms Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T08:51:24.310331Z
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.
Observation c599af39-e52d-4fa2-aad5-f12841744e05 · inbound
TDD Governance for Multi-Agent Code Generation via Prompt Engineering cites this paper.
TDD Governance for Multi-Agent Code Generation via Prompt Engineering Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T09:16:26.937686Z
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.
Observation e0993458-c552-46f1-84a0-6136dcccc9d3 · inbound
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models cites this paper.
Benchmarking Empirical Privacy Protection for Adaptations of Large Language Models Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 35
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-03T00:27:29.548906Z
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.
Observation 8540ef26-6a44-4416-8ca2-fa223cc2e69d · inbound
No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot cites this paper.
No Two Developers Think Alike: How Problem-Solving Styles and Experience Shape Needs in Conversational Interaction with Copilot Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 24
- Resolution
- verified exact
- arxiv_id, observed 2026-07-04T01:49:21.979113Z
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.
Observation d6ad79ec-e9ac-430a-a3b0-86000957642a · inbound
Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns cites this paper.
Comparing Large Language Models on Scrum Certification-Style Questions: Accuracy, Stability, and Error Patterns Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:07:21.016257Z
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.
Observation 70ae1072-d1b1-47b4-afc0-088627c9a665 · inbound
Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study cites this paper.
Prompting GPT-5 on Scrum Certification Questions: An Empirical Accuracy Study Prompt Engineering or Fine-Tuning: An Empirical Assessment of LLMs for Code
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T20:07:21.060144Z
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.