Paper Citation Record · LEDGER

Aligning Large Language Models through Synthetic Feedback

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2305.13735.

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

pith.paper-citation-record.v1
2305.13735 v2

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 2026-08-21T06:32:19.484+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:52:07.009243Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T06:38:37.188436Z

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 68e8bc6c-f75f-410b-9611-a8da166b9c12 · 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 Aligning Large Language Models through Synthetic Feedback

Reference 299

Resolution: verified exact; arxiv_id, observed 2026-05-18T06:38:37.191537Z

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.

Observation 9943dbdd-8938-4107-a15c-1f13f1750abf · inbound

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models cites this paper.

Surveying the Effects of Quality, Diversity, and Complexity in Synthetic Data From Large Language Models Aligning Large Language Models through Synthetic Feedback

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T22:57:01.730077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 35d7099d-f2a2-4220-ba61-a8293455412d · inbound

A Survey on Progress in LLM Alignment from the Perspective of Reward Design cites this paper.

A Survey on Progress in LLM Alignment from the Perspective of Reward Design Aligning Large Language Models through Synthetic Feedback

Reference 117

Resolution: unresolved; no resolver link, observed 2026-08-16T00:52:07.009243Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a60f133-e2c8-4128-bf1d-35452e88f718 · inbound

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives cites this paper.

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives Aligning Large Language Models through Synthetic Feedback

Reference 115

Resolution: unresolved; no resolver link, observed 2026-08-07T04:46:04.456416Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 97eacb36-d7ab-4c1f-9b79-8def244e34cb · inbound

CALMA: A Process for Deriving Context-aligned Axes for Language Model Alignment cites this paper.

CALMA: A Process for Deriving Context-aligned Axes for Language Model Alignment Aligning Large Language Models through Synthetic Feedback

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T18:10:17.344971Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 59a38bb7-c0e5-4670-8850-c1cb56ef7d22 · inbound

Are Today's LLMs Ready to Explain Well-Being Concepts? cites this paper.

Are Today's LLMs Ready to Explain Well-Being Concepts? Aligning Large Language Models through Synthetic Feedback

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T01:02:50.511049Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.