Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2210.04714.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T19:55:12.295475Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T06:41:37.047817Z
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 b7a14054-79b5-40d6-8c98-6510d3976544 · inbound
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data cites this paper.
Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Data Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:59:37.172775Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation dadf7bb8-3dfd-42fe-be69-5e20d9fad8e9 · inbound
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered cites this paper.
From Calibration to Collaboration: LLM Uncertainty Quantification Should Be More Human-Centered Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Reference 1070
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T05:38:27.157672Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 398e3cb9-31f9-4734-941d-a9f5d311d25a · inbound
Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains cites this paper.
Controlling Context: Generative AI at Work in Integrated Circuit Design and Other High-Precision Domains Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Reference 72
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T19:55:12.295475Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fa64768f-9800-4881-b69c-b6db9c0639ca · inbound
Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots cites this paper.
Evaluating Uncertainty and Quality of Visual Language Action-enabled Robots Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Reference 54
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T15:02:25.464560Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 384bcdf3-7290-4d9b-b598-a3cc619dae13 · inbound
Calibrating Model-Based Evaluation Metrics for Summarization cites this paper.
Calibrating Model-Based Evaluation Metrics for Summarization Uncertainty Quantification with Pre-trained Language Models: A Large-Scale Empirical Analysis
Reference 68
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T06:41:37.049046Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.