Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.02261.
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 2026-08-11T06:34:44.6726+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T21:37:05.206179Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T17:08:22.480223Z
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 c63fe1fe-8102-44f7-8550-58385a2a99e8 · inbound
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions cites this paper.
Integrating LLMs with ITS: Recent Advances, Potentials, Challenges, and Future Directions LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
Reference 227
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:37:05.206179Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7f62980e-f379-498f-bf67-b9e70b0515ed · inbound
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models cites this paper.
Whose View of Safety? A Deep DIVE Dataset for Pluralistic Alignment of Text-to-Image Models LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource Languages
Reference 2024
- Resolution
- verified exact
- local_arxiv, observed 2026-08-06T17:08:22.561607Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.