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Paper Citation Record · LEDGER

Task Contamination: Language Models May Not Be Few-Shot Anymore

As of 13 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2312.16337.

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

pith.paper-citation-record.v1
2312.16337 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:19:26.137171Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

6
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 87f5f08a-e9ad-42b8-aa63-bac9830b8a33 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 208

Resolution
verified exact
arxiv_id, observed 2026-05-10T17:34:42.822866Z

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.

source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:6ada5e39047bd588c8dffbe2db0d9cc5828478245c4bb9cfda6040a20e988526

Observation 2b484a52-2013-4f30-adda-d5acefe41291 · inbound

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit cites this paper.

CODECLEANER: Elevating Standards with A Robust Data Contamination Mitigation Toolkit Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-12T19:19:26.137171Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:19:26.137171Z digest=sha256:ffc3f6a3df4e480cdf8c9c99b22ddeeaeba54f278d3f5789d41f1946554c275c

Observation 6828b676-8826-4c47-991f-58d1e2f9bce3 · inbound

Machines of Meaning cites this paper.

Machines of Meaning Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 79

Resolution
unresolved
no resolver link, observed 2026-08-11T18:24:39.626005Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:24:39.626005Z digest=sha256:07503d86f5b7defd236cc2ae1106bd8b6c06afa419bb281fc83841cf85ec19a1

Observation ebe4ec8b-d75c-4c46-b21d-be97a5e8b2fb · inbound

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects cites this paper.

Reality Check: A New Evaluation Ecosystem Is Necessary to Understand AI's Real World Effects Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 60

Resolution
unresolved
no resolver link, observed 2026-08-07T14:26:35.047220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:26:35.047220Z digest=sha256:c895c710b6fe7edc3b06ca76f5d6100547855d68cfe4e692e9e29d5737459b4d

Observation f25342e2-f043-4ccb-9097-b0eac02ab6a7 · inbound

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications cites this paper.

Pretraining Data Exposure in Large Language Models: A Survey of Membership Inference, Data Contamination, and Security Implications Task Contamination: Language Models May Not Be Few-Shot Anymore

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-06-30T17:24:56.600580Z

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.

source=pdf_text observed=2026-06-30T17:20:16.735285Z digest=sha256:0cbb0e479cca98a8975bcfc47bd8dc55800f7c4d0523a617155413b723f6d5b5