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Paper Citation Record · LEDGER

Social Bias Evaluation for Large Language Models Requires Prompt Variations

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 14 inbound Pith citation observations for arXiv:2407.03129.

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

pith.paper-citation-record.v1
2407.03129 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 14 of 14 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 14 of 14 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:54:28.481096Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T12:36:57.034343Z

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 26c961d9-5458-4eb0-855c-399b9c9b23d9 · inbound

Position: Contextual Integrity is Inadequately Applied to Language Models cites this paper.

Position: Contextual Integrity is Inadequately Applied to Language Models Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-09T21:05:08.795859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:05:08.795859Z digest=sha256:314d898c68d9ba3ce237fd0b4fe54ed0cb921534a466310d5bacbe36fdacfc81

Observation f891b6c9-538a-485b-84f3-d56781eaf6a6 · inbound

Token-Level Entropy Reveals Demographic Disparities in Large Language Models cites this paper.

Token-Level Entropy Reveals Demographic Disparities in Large Language Models Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-09T20:34:09.150533Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T20:34:09.150533Z digest=sha256:33a5b016c7e53fa10e1dbae998821d42060ba077722a4ff40c245c9dd9f5e591

Observation 0f2d40c6-135d-4e10-a00e-e81655c7d51b · inbound

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection cites this paper.

Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-08T15:21:49.606793Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:21:49.606793Z digest=sha256:bfb05e820891cdb5053c6334e52168f2cb373a38a4ed7d2a4d6bb84285f45c0c

Observation 23d4cb3b-ead0-4e33-8dfd-d0a07f9b0a86 · inbound

Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languages cites this paper.

Mind the Language Gap: Automated and Augmented Evaluation of Bias in LLMs for High- and Low-Resource Languages Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-16T11:54:28.481096Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:54:28.481096Z digest=sha256:fdb4c979e4a28d3d988365dc0a0ea24582c6bd9cc109e23d079e8e082903d076

Observation dfa945a4-9ccd-4309-9baa-58332369285b · inbound

Advertising in AI systems: Society must be vigilant cites this paper.

Advertising in AI systems: Society must be vigilant Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 2016

Resolution
unresolved
no resolver link, observed 2026-08-07T14:34:30.138329Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:34:30.138329Z digest=sha256:3da06b61b8c8d26334245d0514a35536eec61026388edd8804d61e6b4ed095b4

Observation 61a896a5-5d7e-4e3a-81c7-ad1ee7ebd480 · inbound

ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments cites this paper.

ReliableEval: A Recipe for Stochastic LLM Evaluation via Method of Moments Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-07T13:20:17.246568Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T13:20:17.246568Z digest=sha256:703fd5b9c3fd07639e24e819a6840c0cc8f248115c853e1d54fa6477b684442a

Observation 090ed725-2864-4214-9808-77d96f0b719b · inbound

The Thin Line Between Comprehension and Persuasion in LLMs cites this paper.

The Thin Line Between Comprehension and Persuasion in LLMs Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 1

Resolution
verified exact
arxiv_id, observed 2026-05-19T06:07:07.313989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-19T06:05:44.600781Z digest=sha256:508443abd44560e36645d7c2231fa44330c06012cce165149263788bf0c9a57e

Observation 4d079846-2dcc-4623-a984-16db97d7b9ac · inbound

Obscured but Not Erased: Evaluating Nationality Bias in LLMs via Name-Based Bias Benchmarks cites this paper.

Obscured but Not Erased: Evaluating Nationality Bias in LLMs via Name-Based Bias Benchmarks Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T15:02:35.246985Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:02:35.246985Z digest=sha256:108b783810a6953c5fed829ca0e2a0f456aa212a6bebed5184adbf4654f5f142

Observation fd9a0044-26da-41ef-a0f1-b3a8ee8aaed5 · inbound

The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs cites this paper.

The Personalization Trap: How User Memory Alters Emotional Reasoning in LLMs Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-04T10:26:10.871315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T10:26:10.871315Z digest=sha256:9a61ce97533b3162b36ed05a6259d6662bc87990ea369032767e15b674960cbe

Observation fe39d6de-e4ed-4c5f-ae1d-65aa50d21a26 · inbound

DeFrame: Debiasing Large Language Models Against Framing Effects cites this paper.

DeFrame: Debiasing Large Language Models Against Framing Effects Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-03T04:43:43.654103Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T04:43:43.654103Z digest=sha256:38451ce12ddd61adbaa57e71a363784e4ceb112a6d93f2f2b2c08cceaa7c7405

Observation d35c6e4a-b7ea-44ec-b034-7e158cd267c9 · inbound

VoxSafeBench: Not Just What Is Said, but Who, How, and Where cites this paper.

VoxSafeBench: Not Just What Is Said, but Who, How, and Where Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 41

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T10:24:22.033768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T10:19:28.041282Z digest=sha256:e55e5955ccebd9afcf92eca28167dee1106743f098ec933dc05bee8549f2ce8a

Observation 4fa04ce1-9981-498f-912b-88bf9066468b · inbound

Intersectional Fairness in Large Language Models cites this paper.

Intersectional Fairness in Large Language Models Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 12

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T14:01:06.721903Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-09T23:44:08.610255Z digest=sha256:4ca25ee582dd0e587a095bae8b26e62618e9e410cbbfe07dc585fdc6c1dded19

Observation 78d25cca-bb06-4d44-8aa7-df9072a72313 · inbound

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities cites this paper.

Position: the Stochastic Parrot in the Coal Mine. Model Collapse is a Threat to Low-Resource Communities Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 13

Resolution
metadata mismatch
arxiv_id, observed 2026-05-09T06:35:40.882395Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-08T18:16:51.881163Z digest=sha256:a59d0991cd312c461b098223f51b2eb69cbac0ccd9e8b8aefd3ccb40bf981632

Observation 34bae549-f486-400f-bfcd-457a4a6d443b · inbound

Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting cites this paper.

Can LLMs Be Constrained to the Past? Improving Knowledge Cutoff through Recall-Based Prompting Social Bias Evaluation for Large Language Models Requires Prompt Variations

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-07-02T12:36:57.035937Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-06-28T01:59:09.484201Z digest=sha256:0730f19ab2888c8abd4bebcba81598f4016658cff14dc18a56963369f6f82a3e