Paper Citation Record · LEDGER

Benchmarking Distributional Alignment of Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2411.05403.

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

pith.paper-citation-record.v1
2411.05403 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 2026-08-10T06:31:04.303077+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:00:39.805216Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-09T23:56:38.427353Z

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 5c779e48-fbd9-45cf-943d-6ea3b0060222 · inbound

Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions cites this paper.

Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions Benchmarking Distributional Alignment of Large Language Models

Reference 4

Resolution: verified exact; arxiv_id, observed 2026-05-23T03:02:27.066271Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation f54dd809-6942-49f3-976d-c1885aa907d2 · inbound

Using LLMs to Advance the Cognitive Science of Collectives cites this paper.

Using LLMs to Advance the Cognitive Science of Collectives Benchmarking Distributional Alignment of Large Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T13:00:39.805216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ab5922-76a2-48e2-971d-ecbc638853bf · inbound

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling cites this paper.

Flipping Against All Odds: Reducing LLM Coin Flip Bias via Verbalized Rejection Sampling Benchmarking Distributional Alignment of Large Language Models

Reference 11

Resolution: verified exact; arxiv_id, observed 2026-05-19T09:27:14.822005Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 719e494d-11f6-4e39-ab75-e4eba9cc0091 · inbound

The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models cites this paper.

The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models Benchmarking Distributional Alignment of Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-06T14:58:13.293429Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f94b13a1-43bf-4b11-a859-a4278c180c4e · inbound

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild cites this paper.

Video2Reaction: Mapping Video to Audience Reaction Distribution in the Wild Benchmarking Distributional Alignment of Large Language Models

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-07-09T23:56:38.428611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e12a1ee0-d84f-441e-923e-04c824ee5f84 · inbound

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models cites this paper.

Partition, Prompt, Aggregate: Statistical Self-Consistency in Language Models Benchmarking Distributional Alignment of Large Language Models

Reference 143

Resolution: unresolved; no resolver link, observed 2026-08-01T23:43:19.678964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.