Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.11162.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-03T10:43:50.086229Z
A source-named dated measurement, never combined with another source.
Source: cited_works
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 12ed9818-f4b6-4b66-9861-598e78ff140d · inbound
LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data cites this paper.
LPCAN: Lightweight Pyramid Cross-Attention Network for Rail Surface Defect Detection Using RGB-D Data How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T10:43:50.086229Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2974870d-8efe-4c39-beea-2a3055d39623 · inbound
Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method cites this paper.
Knowledge-Embedded and Hypernetwork-Guided Few-Shot Substation Meter Defect Image Generation Method How Good are LLMs at Relation Extraction under Low-Resource Scenario? Comprehensive Evaluation
Reference 59
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T10:43:42.292852Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.