Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2404.03192.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:43:14.626410Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-15T01:53:28.528692Z
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 28d28c42-321c-489a-9b76-4b68246e9430 · inbound
Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) cites this paper.
Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Reference 93
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:43:14.626410Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 0ec36478-e17c-4e3b-9364-1a256ec128a5 · inbound
The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs cites this paper.
The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T10:17:26.716770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 541b3d8c-4e6f-4aff-bfd4-9d8b13b59ff5 · inbound
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models cites this paper.
How Good are LLM-based Rerankers? An Empirical Analysis of State-of-the-Art Reranking Models Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Reference 42
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:15:15.374649Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7a77a3ea-2396-4a22-8a87-56306f26347a · inbound
Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks cites this paper.
Not All RAGs Are Created Equal: A Component-Wise Empirical Study for Software Engineering Tasks Do Large Language Models Rank Fairly? An Empirical Study on the Fairness of LLMs as Rankers
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-05-15T01:53:28.530521Z
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.