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Paper Citation Record · LEDGER

An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2401.06692.

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

pith.paper-citation-record.v1
2401.06692 v3

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 2026-08-14T06:32:32.682623+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T11:51:02.775290Z

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

0
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 dd4acbb4-355e-4a5d-9a8f-623d5c6118f9 · inbound

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response cites this paper.

RobustFT: Robust Supervised Fine-tuning for Large Language Models under Noisy Response An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-11T11:51:02.775290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T11:51:02.775290Z digest=sha256:f497a4706033e6ca41cd96c260a281edf990c4e8bb7dcbb111aa9eacd43b5dab

Observation 17ea860b-5e66-4673-a0b5-82171a40dc6a · inbound

Efficient Data Selection at Scale via Influence Distillation cites this paper.

Efficient Data Selection at Scale via Influence Distillation An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 34

Resolution
unresolved
no resolver link, observed 2026-08-07T14:25:20.521520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:25:20.521520Z digest=sha256:25b2b1d50bb57b44ebae8ae2ea252a9a858967de85fcb093ecc11527c5437e99

Observation 0d995afe-0be7-454a-860a-0930a3d909bf · inbound

ATGen: A Framework for Active Text Generation cites this paper.

ATGen: A Framework for Active Text Generation An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 5

Resolution
unresolved
no resolver link, observed 2026-08-06T21:49:47.486653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T21:49:47.486653Z digest=sha256:7a638dac38618e313096d91bc87512543b7406e9055f8ee7422141426019cb15

Observation ae3c261c-8784-4f6e-99ba-7bda28ee84e1 · inbound

Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs cites this paper.

Improving Task Diversity in Label Efficient Supervised Finetuning of LLMs An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 3

Resolution
unresolved
no resolver link, observed 2026-08-06T12:47:42.920123Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T12:47:42.920123Z digest=sha256:7b4a9d829e18dfdf41ce98e0124912370aca9dbfadf22d390f3107ef42d5b735

Observation 7058df37-d16b-4192-9ad6-7000d6e7b9d7 · inbound

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization cites this paper.

GRACE: A Dynamic Coreset Selection Framework for Large Language Model Optimization An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-10T20:30:48.939567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

source=pdf_text observed=2026-05-10T18:06:46.131725Z digest=sha256:b8e02a3ad66669b4d411593b0d7ed1bb0f0d3295fb8e08192b396134b8ed9248