Paper Citation Record · LEDGER

Fine-tuning language models to find agreement among humans with diverse preferences

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2211.15006.

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

pith.paper-citation-record.v1
2211.15006 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 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T14:13:06.512905Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T16:54:58.720230Z

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 9bbcb971-1f9f-4dd7-9cc4-5a0a2100e9a3 · inbound

A Roadmap to Pluralistic Alignment cites this paper.

A Roadmap to Pluralistic Alignment Fine-tuning language models to find agreement among humans with diverse preferences

Reference 179

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T14:37:53.587475Z

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 ab324a87-2e5f-4b66-899a-c051a60eefba · inbound

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges cites this paper.

Alignment and Safety in Large Language Models: Safety Mechanisms, Training Paradigms, and Emerging Challenges Fine-tuning language models to find agreement among humans with diverse preferences

Reference 158

Resolution: unresolved; no resolver link, observed 2026-08-06T14:13:06.512905Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f6ba0a34-d610-49ac-a6e6-a99237283578 · inbound

Private, Verifiable, and Auditable AI Systems cites this paper.

Private, Verifiable, and Auditable AI Systems Fine-tuning language models to find agreement among humans with diverse preferences

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-05T15:43:58.730161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5523a00e-870d-43e1-9450-9b9241ebb11a · inbound

Reducing Political Manipulation with Consistency Training cites this paper.

Reducing Political Manipulation with Consistency Training Fine-tuning language models to find agreement among humans with diverse preferences

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-22T05:34:40.227686Z

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 4dafae9a-65f5-4118-8fca-d6ad696df10a · inbound

Reducing Political Manipulation with Consistency Training cites this paper.

Reducing Political Manipulation with Consistency Training Fine-tuning language models to find agreement among humans with diverse preferences

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-30T16:54:58.722611Z

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 996524c1-8261-4f57-9c33-36780dec384a · inbound

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback cites this paper.

LEMUR: Learning to Align with Multi-Objective Reinforcement Learning from Preference Feedback Fine-tuning language models to find agreement among humans with diverse preferences

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-03T04:39:30.705616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.