Paper Citation Record · LEDGER

Fairness in LLM-Generated Surveys

As of 10 August 2026, this Paper Citation Record lists 37 of 37 outbound references and 2 inbound Pith citation observations for arXiv:2501.15351.

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

pith.paper-citation-record.v1
2501.15351 v1

Coverage vector

measured 37 of 37 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-10T14:25:39.409747Z

measured 39 of 39 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T05:34:28.139585Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T14:17:44.199745Z

Reference resolution

37 of 37 outbound references displayed

verified exact4
verified fuzzy20
unresolved12
parse uncertain0
malformed identifier1
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 9bbdbeda-e3f9-4d7b-8c5e-4184d68697ce · outbound

This paper cites an unresolved cited work.

Fairness in LLM-Generated Surveys Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:25:40.177251Z

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 cdfbd17c-8b43-4062-bc77-0bc1f169f2dc · outbound

This paper cites Political Analysis 31, 3 (2023), 337–351.

Fairness in LLM-Generated Surveys Political Analysis 31, 3 (2023), 337–351

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.143583Z

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 5a7a97fc-c405-40d7-909b-514c117fffd3 · outbound

This paper cites https://doi.org/10.1073/pnas.2314021121 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.2314021121 [Bargsted and Maldonado(2018)] Matías Bargsted and Luis Maldonado.

Fairness in LLM-Generated Surveys https://doi.org/10.1073/pnas.2314021121 arXiv:https://www.pnas.org/doi/pdf/10.1073/pnas.2314021121 [Bargsted and Maldonado(2018)] Matías Bargsted and Luis Maldonado

Reference 5

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Source-reported events for the cited work

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Observation 99fe7bfd-d41d-4a32-957f-8138acfc9bb9 · outbound

This paper cites MIT press.

Fairness in LLM-Generated Surveys MIT press

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.109104Z

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

Observation 9e3b7ef7-e6c8-44d2-a77a-6dcd4b59ca7b · outbound

This paper cites an unresolved cited work.

Fairness in LLM-Generated Surveys Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:25:40.057459Z

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 43755cdd-d780-4360-b724-5d696202a70f · outbound

This paper cites [Horton(2023)] John J Horton.

Fairness in LLM-Generated Surveys [Horton(2023)] John J Horton

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.023722Z

Source-reported events for the cited work

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Observation b779879a-50c4-450d-8a60-7f6459680909 · outbound

This paper cites National Bureau of Economic Research.

Fairness in LLM-Generated Surveys National Bureau of Economic Research

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.007396Z

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 89e9ffb8-50f7-40cf-bd08-4eed31860d0a · outbound

This paper cites Language Models (Mostly) Know What They Know.

Fairness in LLM-Generated Surveys Language Models (Mostly) Know What They Know

Reference 16

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Observation 31738598-ee77-4031-ae79-b5493206819f · outbound

This paper cites In International conference on machine learning.

Fairness in LLM-Generated Surveys In International conference on machine learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.989969Z

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 cb94d899-aab4-483a-92f0-06cdd9c6cb07 · outbound

This paper cites AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction.

Fairness in LLM-Generated Surveys AI-Augmented Surveys: Leveraging Large Language Models and Surveys for Opinion Prediction

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-10T14:25:39.308815Z

Source-reported events for the cited work

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Observation 64716c39-0715-46c8-abd0-636da9f1efc7 · outbound

This paper cites [Mehrabi et al.(2021)] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan.

Fairness in LLM-Generated Surveys [Mehrabi et al.(2021)] Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, Kristina Lerman, and Aram Galstyan

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.953255Z

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 0dfe24f9-054b-4425-acd8-3d749f226553 · outbound

This paper cites ACM computing surveys (CSUR) 54, 6 (2021), 1–35.

Fairness in LLM-Generated Surveys ACM computing surveys (CSUR) 54, 6 (2021), 1–35

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.936175Z

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 90d18cc4-dc74-46e9-96a6-4f51c0ba08c2 · outbound

This paper cites BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages.

Fairness in LLM-Generated Surveys BLEnD: A Benchmark for LLMs on Everyday Knowledge in Diverse Cultures and Languages

Reference 22

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Source-reported events for the cited work

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Observation 372e0eb0-6568-4f36-b470-5bb9911932b7 · outbound

This paper cites ACM Journal of Data and Information Quality 15, 2 (2023), 1–21.

Fairness in LLM-Generated Surveys ACM Journal of Data and Information Quality 15, 2 (2023), 1–21

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.919500Z

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 e83e1292-df32-4bee-a1e9-665449d198cb · outbound

This paper cites LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals.

Fairness in LLM-Generated Surveys LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:25:39.336277Z

Source-reported events for the cited work

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Observation 370d8e8c-9a9e-4651-8271-4899cf91c864 · outbound

This paper cites InFindings of the Association for Computational Lin- guistics: NAACL 2024, Kevin Duh, Helena Gomez, and Steven Bethard (Eds.).

Fairness in LLM-Generated Surveys InFindings of the Association for Computational Lin- guistics: NAACL 2024, Kevin Duh, Helena Gomez, and Steven Bethard (Eds.)

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-10T14:25:39.342902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 49b49bfd-e438-4cf8-a01d-c28485b22645 · outbound

This paper cites Advances in Neural Information Processing Systems 36 (2024).

Fairness in LLM-Generated Surveys Advances in Neural Information Processing Systems 36 (2024)

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.900890Z

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 e79afe01-af14-4da9-b007-91b822085246 · outbound

This paper cites In International Conference on Machine Learning.

Fairness in LLM-Generated Surveys In International Conference on Machine Learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.882472Z

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 111df99f-611a-4a4a-9c14-36e83945497d · outbound

This paper cites an unresolved cited work.

Fairness in LLM-Generated Surveys Unresolved cited work

Reference 28

Resolution: unresolved; raw_fallback, observed 2026-08-10T14:25:39.863953Z

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 1a6bba2f-8dbd-4e29-9f4e-f3e8907ce0e3 · outbound

This paper cites In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing.

Fairness in LLM-Generated Surveys In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.847105Z

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 317253c4-c93b-4ad8-ba70-da44b2008750 · outbound

This paper cites In Proceedings of the international workshop on software fairness.

Fairness in LLM-Generated Surveys In Proceedings of the international workshop on software fairness

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.829073Z

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 1a805c13-e8a6-4891-af85-1e236da2a99a · outbound

This paper cites Large language models that replace human participants can harmfully misportray and flatten identity groups.

Fairness in LLM-Generated Surveys Large language models that replace human participants can harmfully misportray and flatten identity groups

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-10T14:25:39.376946Z

Source-reported events for the cited work

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Observation c4c54293-6b8b-41b3-8d36-1796c4c6dd58 · outbound

This paper cites In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency.

Fairness in LLM-Generated Surveys In Proceedings of the 2022 ACM Conference on Fairness, Accountability, and Transparency

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.812416Z

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 75501ac4-5702-4131-ad14-bb4ee1621790 · outbound

This paper cites Political Behavior 44 (2020), 807–838.

Fairness in LLM-Generated Surveys Political Behavior 44 (2020), 807–838

Reference 34

Resolution: verified exact; doi, observed 2026-08-10T14:25:39.456291Z

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 fe2c91a6-6c52-4af8-baae-6dd7dfee9b04 · outbound

This paper cites https://proceedings.neurips.cc/ paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf [West and Iyengar(2020)] Emily A.

Fairness in LLM-Generated Surveys https://proceedings.neurips.cc/ paper_files/paper/2022/file/9d5609613524ecf4f15af0f7b31abca4-Paper-Conference.pdf [West and Iyengar(2020)] Emily A

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.795212Z

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 51f98f42-2cfa-45e3-a126-395c41f1abe0 · outbound

This paper cites PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts.

Fairness in LLM-Generated Surveys PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts

Reference 36

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Source-reported events for the cited work

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Observation 774464ca-b3ac-4a37-b25b-9437535db850 · outbound

This paper cites ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs.

Fairness in LLM-Generated Surveys ProSA: Assessing and Understanding the Prompt Sensitivity of LLMs

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-10T14:25:39.403626Z

Source-reported events for the cited work

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Observation da53ef48-8fd4-4d5e-bdb0-509246a66be7 · outbound

This paper cites 14 Fairness in LLM-Generated Surveys A Appendix A.1 Model Comparison Figure 2: Mean Accuracy and Jensen-Shannon Similarity (JSS) across Socio-Demographic Groups for all Models.

Fairness in LLM-Generated Surveys 14 Fairness in LLM-Generated Surveys A Appendix A.1 Model Comparison Figure 2: Mean Accuracy and Jensen-Shannon Similarity (JSS) across Socio-Demographic Groups for all Models

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.777922Z

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 7e9fd00a-3e55-4fab-891e-468192de6274 · outbound

This paper cites Journal of Management Information Systems 17, 4 (2001), 223–249.

Fairness in LLM-Generated Surveys Journal of Management Information Systems 17, 4 (2001), 223–249

Reference 2001

Resolution: verified exact; raw_fallback, observed 2026-08-10T14:25:39.752865Z

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 93e05377-5f99-4c1a-8ffc-f3dba3db6244 · outbound

This paper cites Presidential Studies Quarterly 43 (2013), 688–708.

Fairness in LLM-Generated Surveys Presidential Studies Quarterly 43 (2013), 688–708

Reference 2013

Resolution: verified exact; doi, observed 2026-08-10T14:25:39.487095Z

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 82a90117-05ba-4b3c-804d-35079b644923 · outbound

This paper cites Journal of Politics in Latin America 10 (2018), 29–68.

Fairness in LLM-Generated Surveys Journal of Politics in Latin America 10 (2018), 29–68

Reference 2018

Resolution: verified exact; doi, observed 2026-08-10T14:25:39.505277Z

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 d58eaaec-2222-4c32-955f-444f12d32936 · outbound

This paper cites In Proceedings of the conference on fairness, accountability, and transparency.

Fairness in LLM-Generated Surveys In Proceedings of the conference on fairness, accountability, and transparency

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:39.972202Z

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 9066bc4a-e8cc-4387-96f3-e13ad51de88e · outbound

This paper cites Advances in neural information processing systems 33 (2020), 1877–1901.

Fairness in LLM-Generated Surveys Advances in neural information processing systems 33 (2020), 1877–1901

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.075423Z

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 dfcce198-de22-4d7c-95db-d4adee1bdebe · outbound

This paper cites Sociological Methods & Research 50, 1 (2021), 3–44.

Fairness in LLM-Generated Surveys Sociological Methods & Research 50, 1 (2021), 3–44

Reference 2021

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.092165Z

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 d2c6f831-5014-4294-b978-040bbcb3208c · outbound

This paper cites Journal of Politics in Latin America 14, 1 (April 2022), 3–30.

Fairness in LLM-Generated Surveys Journal of Politics in Latin America 14, 1 (April 2022), 3–30

Reference 2022

Resolution: malformed identifier; raw_fallback, observed 2026-08-10T14:25:40.040702Z

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 47b8b4ed-5c1b-4591-b761-76a3f30439b2 · outbound

This paper cites In International Conference on Machine Learning.

Fairness in LLM-Generated Surveys In International Conference on Machine Learning

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.160708Z

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 20472b83-e549-4b7c-8ec9-706007cc5c1b · outbound

This paper cites Technical Report.

Fairness in LLM-Generated Surveys Technical Report

Reference 2024

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:25:40.126885Z

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.

Pith citing papers

Observation e0b8416e-6f33-4325-9d21-b0c05dbf8f6b · inbound

Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods cites this paper.

Recalibrating the Compass: Integrating Large Language Models into Classical Research Methods Fairness in LLM-Generated Surveys

Reference 29

Resolution: verified exact; local_arxiv, observed 2026-08-07T14:17:44.248193Z

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 7c5c3aa6-b646-4a95-9616-85f0d5d842b5 · inbound

People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe cites this paper.

People Are Not Just Their Countries. Disentangling Social Determinants of LLM Value Alignment Across Europe Fairness in LLM-Generated Surveys

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-10T05:34:28.139585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.