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Paper Citation Record · LEDGER

Instruction Tuned Models are Quick Learners

As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2306.05539.

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

pith.paper-citation-record.v1
2306.05539 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T21:05:20.552884Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-21T00:33:52.617824Z

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 a0928a73-0771-4c5b-96f7-4043cf3488ef · inbound

Scaling Data-Constrained Language Models cites this paper.

Scaling Data-Constrained Language Models Instruction Tuned Models are Quick Learners

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-18T01:35:21.624496Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-18T01:35:21.150772Z digest=sha256:823c967426189ec7b438b5ab6088dc0b8152d9cb90ab58533f9c1f7f5bf0d226

Observation d9ffc810-fbd2-4d36-9a4d-1ad423795794 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Instruction Tuned Models are Quick Learners

Reference 183

Resolution
verified exact
arxiv_id, observed 2026-05-19T20:28:39.581837Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=pdf_text observed=2026-05-19T20:28:38.900026Z digest=sha256:d07fa4ac72c4b452fa5b14ff722e665d93a851a99be5e0458166a9d8e3200399

Observation 002c5b64-2eb4-41c4-a25f-f5b60b5e824f · inbound

Steps are all you need: Rethinking STEM Education with Prompt Engineering cites this paper.

Steps are all you need: Rethinking STEM Education with Prompt Engineering Instruction Tuned Models are Quick Learners

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-11T21:05:20.552884Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T21:05:20.552884Z digest=sha256:7f9efbb6bf2e71fb94a8d552db06ce3b84b485219b847951a254054e070b1bb3

Observation 3897941f-c0d4-475a-801d-b9a3ae495d14 · inbound

Adapting Biomedical Abstracts into Plain language using Large Language Models cites this paper.

Adapting Biomedical Abstracts into Plain language using Large Language Models Instruction Tuned Models are Quick Learners

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-10T14:05:55.180619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T14:05:55.180619Z digest=sha256:2bc8b9c2a4ee3cdae7880681d08c1f312c928d982c9b259e0ab6002a77e6e5a2

Observation 0866f7eb-9f16-49a9-9058-c3927b6ab5f6 · inbound

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective cites this paper.

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective Instruction Tuned Models are Quick Learners

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:46:12.565635Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-08T08:06:25.436395Z digest=sha256:7483073988b00b503d45319f7bf6a1f650deb47742b8f08fa1866ef60e91ed9f

Observation 66bffa99-4871-45a8-80c5-1d6b91c4f34b · inbound

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective cites this paper.

Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective Instruction Tuned Models are Quick Learners

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-21T00:33:52.619334Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

source=arxiv_source observed=2026-05-21T00:33:22.181383Z digest=sha256:5020c5b473ded06bec2d1f137375241988ddaca08ba40fcdafd4f882b4689a7a