Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2302.00618.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T23:59:12.427599Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T06:38:37.031877Z
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 d8ae706c-6d49-4f2f-abeb-44f70b0d3904 · inbound
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines cites this paper.
DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Reference 49
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-05-11T18:57:47.052355Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 03d533d6-15e1-41ac-90b2-2b6dae79a343 · inbound
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models cites this paper.
Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Reference 249
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T06:38:37.034746Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 20cdd47f-a35c-40c9-a409-b0448f696754 · inbound
Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models cites this paper.
Synthline: A Product Line Approach for Synthetic Requirements Engineering Data Generation using Large Language Models Synthetic Prompting: Generating Chain-of-Thought Demonstrations for Large Language Models
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T23:59:12.427599Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.