Paper Citation Record · LEDGER

Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2305.16938.

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

pith.paper-citation-record.v1
2305.16938 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:25:30.394417Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T22:26:13.497894Z

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 1a023700-31db-4bd3-be62-7c1147e17792 · inbound

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models cites this paper.

Mimicking or Reasoning: Rethinking Multi-Modal In-Context Learning in Vision-Language Models Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T05:25:30.394417Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e11dbe8c-29d4-4e63-b8e3-453edbb4dd88 · inbound

Few-Shot Learning in Video and 3D Object Detection: A Survey cites this paper.

Few-Shot Learning in Video and 3D Object Detection: A Survey Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T15:00:44.421045Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad5cfd56-c41e-4cef-a072-37ecd411761f · inbound

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts cites this paper.

MHA-RAG: Improving Efficiency, Accuracy, and Consistency by Encoding Exemplars as Soft Prompts Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-04T11:23:51.133536Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b4282c4-4aec-47b8-84fd-457e55903d29 · inbound

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study cites this paper.

Leveraging LLMs for Multi-File DSL Code Generation: An Industrial Case Study Few-shot Fine-tuning vs. In-context Learning: A Fair Comparison and Evaluation

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:26:13.500131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.