Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2110.05367.
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 2026-08-10T06:31:04.303077+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T18:51:07.321450Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T12:14:26.525486Z
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 0a2cab10-6e8a-4661-aaeb-31438aa8795f · inbound
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model cites this paper.
Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
Reference 14
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T12:14:26.529058Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.
Observation f1f80435-a67e-424e-9da9-3e77e1cc164d · inbound
Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs cites this paper.
Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
Reference 1983
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T18:51:07.321450Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.