Paper Citation Record · LEDGER

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data

As of 8 August 2026, this Paper Citation Record lists 5 of 5 outbound references and 1 inbound Pith citation observation for arXiv:2505.21997.

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

pith.paper-citation-record.v1
2505.21997 v1

Coverage vector

measured 5 of 5 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:22:14.774135Z

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-19T20:22:55.750693Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

5 of 5 outbound references displayed

verified exact1
verified fuzzy0
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 32f8e7fe-0b4e-42a5-ba05-aa9121a9e6b4 · outbound

This paper cites an unresolved cited work.

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:22:15.681699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation f43fd609-fe65-4f21-8a4e-0a5913e295f4 · outbound

This paper cites an unresolved cited work.

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:22:15.342954Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation fe9658b8-9652-446a-bfc0-e1e590e65d77 · outbound

This paper cites ashamed”, “failure.

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data ashamed”, “failure

Reference 3

Resolution: verified exact; doi, observed 2026-08-07T13:22:15.064549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 36898298-e92e-4a24-aba1-eff7820e5a8b · outbound

This paper cites LLMs generate structurally realistic social networks but overestimate political homophily.

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data LLMs generate structurally realistic social networks but overestimate political homophily

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-07T13:22:14.679815Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 12dc5b8f-4d47-4fb0-9f83-3aa9a304c02a · outbound

This paper cites A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology.

Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data A Comparative Study of Open-Source Large Language Models, GPT-4 and Claude 2: Multiple-Choice Test Taking in Nephrology

Reference 1317

Resolution: unresolved; no resolver link, observed 2026-08-07T13:22:14.774135Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation abccaee1-874b-4e16-84d3-57a395c3f678 · inbound

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning cites this paper.

Response-free item difficulty modelling for multiple-choice items with fine-tuned transformers: Component-wise representation and multi-task learning Leveraging Interview-Informed LLMs to Model Survey Responses: Comparative Insights from AI-Generated and Human Data

Reference 190

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:23:12.757221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.