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Paper Citation Record · LEDGER

Fine-tune Language Models to Approximate Unbiased In-context Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2310.03331.

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

pith.paper-citation-record.v1
2310.03331 v1

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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T23:52:00.865170Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T18:00:50.356195Z

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 dbd0056a-f512-4b0a-b794-b6b3ae6e4a65 · inbound

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models cites this paper.

H$_2$O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models Fine-tune Language Models to Approximate Unbiased In-context Learning

Reference 120

Resolution
verified exact
arxiv_id, observed 2026-05-17T18:00:50.358747Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-17T18:00:50.053377Z digest=sha256:731f9290b7081ffce86c530da6517a012eff12507c964ef1c5f2edb8b326cf85

Observation 1aff218c-600b-4c08-af07-0e73b6f5ab9b · inbound

Exploring Imbalanced Annotations for Effective In-Context Learning cites this paper.

Exploring Imbalanced Annotations for Effective In-Context Learning Fine-tune Language Models to Approximate Unbiased In-context Learning

Reference 58

Resolution
unresolved
no resolver link, observed 2026-08-08T23:52:00.865170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T23:52:00.865170Z digest=sha256:3a7d26e895cc225873dbe969b64718589b25f41718e2aca04c9031c929ceff3d