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Paper Citation Record · LEDGER

A Study on the Calibration of In-context Learning

As of 21 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2312.04021.

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

pith.paper-citation-record.v1
2312.04021 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T14:18:05.736371Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T06:41:37.082922Z

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 6bad817f-f3dc-46af-b1e3-36e8136e8e59 · inbound

Logits are All We Need to Adapt Closed Models cites this paper.

Logits are All We Need to Adapt Closed Models A Study on the Calibration of In-context Learning

Reference 62

Resolution
unresolved
no resolver link, observed 2026-08-09T14:18:05.736371Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T14:18:05.736371Z digest=sha256:7b624307f7492993296d701133cb513ad499cef75a8ba4b6996584353640de3b

Observation b9d8a428-c5fb-408c-9165-739eadbbecc7 · inbound

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges cites this paper.

Fine-Tuning and Prompt Engineering of LLMs, for the Creation of Multi-Agent AI for Addressing Sustainable Protein Production Challenges A Study on the Calibration of In-context Learning

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-06T22:49:26.569651Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:49:26.569651Z digest=sha256:cc28a46fe918d719d5b4b090823e2ca8394b7aa0a8644e28abbfd432a6d2c491

Observation 3d314c8f-a778-4a4b-95fb-58deabdfb882 · inbound

Calibrating Model-Based Evaluation Metrics for Summarization cites this paper.

Calibrating Model-Based Evaluation Metrics for Summarization A Study on the Calibration of In-context Learning

Reference 72

Resolution
verified exact
arxiv_id, observed 2026-05-10T06:41:37.084199Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=arxiv_source observed=2026-05-10T06:36:55.334742Z digest=sha256:d962772e8c4c758add0d61f872e692c4440cfd303faef06cc1c6f1934e31eb15