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Paper Citation Record · LEDGER

FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.10147.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
1810.10147 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-05T11:01:20.716385Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: pith, observed 2026-08-05T11:01:21.019258Z

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 897b0e9f-4ec2-42d3-ad53-81c119e8f2a5 · inbound

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning cites this paper.

Learning Mechanism Underlying NLP Pre-Training and Fine-Tuning FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Reference 58

Resolution
verified exact
local_arxiv, observed 2026-08-05T11:01:21.031563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-08-05T11:01:20.716385Z digest=sha256:db78de49727cbb923812b7bf8ef3ef44f9da919ce1140b97b97c829e35ff8aaf

Observation 8d39323f-e64d-429f-abf4-1048cb2d4f7d · inbound

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals cites this paper.

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-04T20:11:41.618338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T20:11:41.618338Z digest=sha256:a87e864b33f0a1369b3be6c0e3d9ce2b1cd25825ce56dc5bbca7dfa63451f993