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Paper Citation Record · LEDGER

A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2409.16430.

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

pith.paper-citation-record.v1
2409.16430 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:55:42.939261Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T21:47:28.290502Z

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 459f8561-a889-4017-9f56-052ad65e7e06 · inbound

Towards Efficient and Effective Alignment of Large Language Models cites this paper.

Towards Efficient and Effective Alignment of Large Language Models A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 149

Resolution
unresolved
no resolver link, observed 2026-08-07T04:55:42.939261Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:55:42.939261Z digest=sha256:9a0ec6305cb5ed6be387bf63fdbdeaf93da8e88149964c0d30f1742f45355e2d

Observation 9014d36e-5e71-499f-b0c3-d3c4dbd6ca9c · inbound

Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings cites this paper.

Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 69

Resolution
unresolved
no resolver link, observed 2026-08-06T22:50:00.777747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:50:00.777747Z digest=sha256:53f5d5044264505202b12f406e9bd126921929d580e59fae614830b1f1f6b2f4

Observation aff0cdc6-48d9-4b2d-a340-f122735f9e77 · inbound

Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing cites this paper.

Audit, Alignment, and Optimization of LM-Powered Subroutines with Application to Public Comment Processing A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-06T18:33:05.098462Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T18:33:05.098462Z digest=sha256:f115989a8b2ce3eacfef6c65c7b3cd4b63005278793b58a5550d3d55ac5c76d6

Observation f3782f33-dcdc-48d6-9ac8-6bdf14f117b7 · inbound

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models cites this paper.

LLMs on Trial: Evaluating Judicial Fairness for Large Language Models A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-06T17:29:44.708603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:29:44.708603Z digest=sha256:82029a94f5384c3cce3b9db031b7d03bcfd9ff3e294d0a15c40a96c7c88d546f

Observation 3a13feb6-d4e4-40fb-a239-1414680108fb · inbound

A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories cites this paper.

A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 21

Resolution
unresolved
no resolver link, observed 2026-08-05T20:57:56.301512Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T20:57:56.301512Z digest=sha256:cbcf393e7b628829b6a8c389a905e9c21df94c2a2cefccfb0096363176ab62e1

Observation 9be2abda-05eb-43fd-9968-ac1a482be668 · inbound

MLLM-as-a-Judge Exhibits Model Preference Bias cites this paper.

MLLM-as-a-Judge Exhibits Model Preference Bias A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 44

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T10:01:00.229100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T15:42:39.226895Z digest=sha256:44ce830c69c1209260ee499d0ca9048079a3dbf082d7c1e91ba15ac89f16a54d

Observation 769af6c9-9620-41d3-a901-bf88dbd9536d · inbound

Why Do Large Language Models Generate Harmful Content? cites this paper.

Why Do Large Language Models Generate Harmful Content? A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-11T10:21:04.795409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T15:31:13.545599Z digest=sha256:3213d537bd74d16f7eabbedfa41db2d73acc8ebc2de264777fea04ab51838c02

Observation 2d4a488e-13d9-44e1-b18d-50ade819ad85 · inbound

First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows cites this paper.

First, Do No Harm (With LLMs): Mitigating Racial Bias via Agentic Workflows A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 4

Resolution
metadata mismatch
arxiv_id, observed 2026-05-11T12:21:06.147028Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T03:47:54.377400Z digest=sha256:03eda9e2d91c7a368c836f11a9d9b388205380fd5e616f555d833bb26869a510

Observation 78abf404-383f-49ce-a905-f1b084fa428d · inbound

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models cites this paper.

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 2

Resolution
metadata mismatch
arxiv_id, observed 2026-05-15T00:28:23.602902Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-15T00:25:04.460757Z digest=sha256:a3e4916663f8f56a961726c704bdb27ce53615985edba4bb8684ee85f860d2f0

Observation dcf8444d-582f-495d-aab3-5d3831fb09f9 · inbound

Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation cites this paper.

Fairness-Aware Retrieval Optimization for Retrieval-Augmented Generation A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 7

Resolution
verified exact
arxiv_id, observed 2026-05-19T19:27:43.493846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T19:25:59.663956Z digest=sha256:b84d4b9f353d4fce9c926ca0531c26eb64d04bdca9494ae08d185fb773e54b4e

Observation 4e1708be-b078-4d3e-af8e-f5dbaf788373 · inbound

Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning cites this paper.

Curation of a Cardiology Interface Terminology for Highlighting Electronic Health Records using Machine Learning A Comprehensive Survey of Bias in LLMs: Current Landscape and Future Directions

Reference 56

Resolution
verified exact
arxiv_id, observed 2026-07-02T21:47:28.291844Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T19:26:11.516070Z digest=sha256:a1a7ac94c4b5a329f44b7e369e1b3e6f35ac4dc58e5967cc9c9db9bb28b66e53