Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.14607.
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 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T05:18:55.816962Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-11T21:22:47.585095Z
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 1fab40d1-3c53-4edd-af39-bc2a233f10b7 · inbound
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective cites this paper.
Unveiling Performance Challenges of Large Language Models in Low-Resource Healthcare: A Demographic Fairness Perspective Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T05:18:55.816962Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 60563bee-ec35-4488-a109-7cc4bb77efab · inbound
Improving LLM Group Fairness on Tabular Data via In-Context Learning cites this paper.
Improving LLM Group Fairness on Tabular Data via In-Context Learning Confronting LLMs with Traditional ML: Rethinking the Fairness of Large Language Models in Tabular Classifications
Reference 45
- Resolution
- verified exact
- local_arxiv, observed 2026-08-11T21:22:47.624830Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.