Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2104.08726.
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 2026-08-20T06:33:59.587034+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:59:48.196151Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-15T20:53:04.596332Z
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 9cf526c1-3e8c-4483-9c5c-04ac7672c8b8 · inbound
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models cites this paper.
PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Reference 154
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:59:48.196151Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 79a52402-cdab-411d-bc6a-92972106c00f · inbound
Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks cites this paper.
Multilingual Prompt Engineering in Large Language Models: A Survey Across NLP Tasks AmericasNLI: Evaluating Zero-shot Natural Language Understanding of Pretrained Multilingual Models in Truly Low-resource Languages
Reference 2024
- Resolution
- verified exact
- local_arxiv, observed 2026-08-15T20:53:04.601390Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.