Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Can We Use Large Language Models to Fill Relevance Judgment Holes?
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2405.05600.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T12:42:47.373137Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T14:22:59.155730Z
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 896e37bd-4420-440b-b58a-955634184988 · inbound
MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering cites this paper.
MedPAIR: Measuring Physicians and AI Relevance Alignment in Medical Question Answering Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T12:42:47.373137Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 32e5d81f-779f-43c0-828d-0c2eccee9b01 · inbound
Leveraging LLMs to Evaluate Usefulness of Document cites this paper.
Leveraging LLMs to Evaluate Usefulness of Document Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:11:38.203547Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f6d2a5e3-6d9b-48e0-b5e0-1141a96a4997 · inbound
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement cites this paper.
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:11:48.297439Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9ee5d5b-1a92-4d24-bd32-664317f39040 · inbound
Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications cites this paper.
Reliable Annotations with Less Effort: Evaluating LLM-Human Collaboration in Search Clarifications Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:19:29.629492Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 16667c40-3204-4412-9b6f-0b6354f2d896 · inbound
When LLMs Disagree: Diagnosing Relevance Filtering Bias and Retrieval Divergence in SDG Search cites this paper.
When LLMs Disagree: Diagnosing Relevance Filtering Bias and Retrieval Divergence in SDG Search Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:42:31.751568Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 3d61a8f9-4c98-414f-a52b-df08af21d2a5 · inbound
Measuring Hypothesis Testing Errors in the Evaluation of Retrieval Systems cites this paper.
Measuring Hypothesis Testing Errors in the Evaluation of Retrieval Systems Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:34:45.899164Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 4e55de38-3c98-46fa-bc43-2ca872946aeb · inbound
Criteria-Based LLM Relevance Judgments cites this paper.
Criteria-Based LLM Relevance Judgments Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 1
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:00:21.149269Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dd2b07aa-6b26-439d-bb3f-761eb1037303 · inbound
LLMs as Assessors: Right for the Right Reason? cites this paper.
LLMs as Assessors: Right for the Right Reason? Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T14:22:59.158283Z
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 8acaeac9-154b-42eb-aa3b-5c99e54b00a8 · inbound
Hybrid Pooling with LLMs via Relevance Context Learning cites this paper.
Hybrid Pooling with LLMs via Relevance Context Learning Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T06:07:25.688182Z
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 70ecc1b3-0e4b-4d65-856b-96235429e614 · inbound
Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search cites this paper.
Advancing Relevance Measurement with Vision-Language Models for Web-Scale Search Can We Use Large Language Models to Fill Relevance Judgment Holes?
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-04T07:15:51.836938Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.