Paper Citation Record · LEDGER

Does Synthetic Data Make Large Language Models More Efficient?

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.07830.

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

pith.paper-citation-record.v1
2310.07830 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:46:43.540510Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T09:19:42.880691Z

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 7ee53ea6-57d1-4f77-9c1f-758ab2e69e61 · inbound

SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub cites this paper.

SkillScope: A Tool to Predict Fine-Grained Skills Needed to Solve Issues on GitHub Does Synthetic Data Make Large Language Models More Efficient?

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-10T13:54:24.127936Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 521437f8-a13c-44f8-8f44-49e4f0c7b42c · inbound

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs cites this paper.

A Case Study Exploring the Current Landscape of Synthetic Medical Record Generation with Commercial LLMs Does Synthetic Data Make Large Language Models More Efficient?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:46:43.540510Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 88b9461b-ed77-4fab-9f36-f50bfba70bbf · inbound

RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs cites this paper.

RouteNator: A Router-Based Multi-Modal Architecture for Generating Synthetic Training Data for Function Calling LLMs Does Synthetic Data Make Large Language Models More Efficient?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-15T21:11:18.250109Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d187bb63-974f-49f5-b16d-3e93ad4cc42b · inbound

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction cites this paper.

Sub-Billion, Super-Frontier: Small Language Models Rival Zero-Shot Frontier LLMs on General and Literary Relation Extraction Does Synthetic Data Make Large Language Models More Efficient?

Reference 218

Resolution: verified exact; arxiv_id, observed 2026-07-04T09:19:42.882643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.