Paper Citation Record · LEDGER

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness

As of 15 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2607.22969.

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

pith.paper-citation-record.v1
2607.22969 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-01T04:05:08.797745Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

15 of 15 outbound references displayed

verified exact0
verified fuzzy0
unresolved15
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 19e0eaea-3e05-4199-814f-e6341c47139f · outbound

This paper cites Language models are few-shot learners,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Language models are few-shot learners,

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.627663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d5579811-861d-4f49-9774-81bad4c22764 · outbound

This paper cites Scaling Laws for Neural Language Models.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Scaling Laws for Neural Language Models

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.691619Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b980227d-b516-4dce-8f0c-425ab6d57ca9 · outbound

This paper cites Training Compute-Optimal Large Language Models.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Training Compute-Optimal Large Language Models

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.761579Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9fc17ee-2361-4596-ba3f-439b27cd2d39 · outbound

This paper cites Rethinking the role of demonstrations: What makes in-context learning work?.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Rethinking the role of demonstrations: What makes in-context learning work?

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.830616Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34e0c206-ed7f-40ea-9b1e-026586f62c1b · outbound

This paper cites An Explanation of In-context Learning as Implicit Bayesian Inference.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness An Explanation of In-context Learning as Implicit Bayesian Inference

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.917070Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fc94f1c-2bb4-4f20-a8f1-36916d6ea0ae · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Chain-of-thought prompting elicits reasoning in large language models,

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:07.976611Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f9818dd2-95c4-4185-9c74-018855a172ef · outbound

This paper cites Calibrate before use: Improving few-shot performance of language models,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Calibrate before use: Improving few-shot performance of language models,

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.084179Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bff633c3-b1b0-4a1e-a0c4-8b3d64fd8cf2 · outbound

This paper cites Fan- tastically ordered prompts and where to find them: Overcoming few-shot prompt sensitivity with ensemble transfer learning,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Fan- tastically ordered prompts and where to find them: Overcoming few-shot prompt sensitivity with ensemble transfer learning,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.144177Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a095c685-7c32-4a12-97ef-b6527045f256 · outbound

This paper cites A Survey on In-context Learning.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness A Survey on In-context Learning

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.198132Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a96006a-15e8-47d6-a3d9-5e21235e91c3 · outbound

This paper cites What Makes Good In-Context Examples for GPT-$3$?.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness What Makes Good In-Context Examples for GPT-$3$?

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.304338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 52d3957a-105e-4701-a8f9-1df13693869a · outbound

This paper cites How to fine-tune BERT for text classification?.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness How to fine-tune BERT for text classification?

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.463202Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2bcf2c66-11e6-4597-81a2-da6de714613b · outbound

This paper cites Character-level convolutional networks for text classification,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Character-level convolutional networks for text classification,

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.572817Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1dc9f541-5391-44e2-8190-8f05dd791d7b · outbound

This paper cites Holistic Evaluation of Language Models.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Holistic Evaluation of Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.633279Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9654d69b-d43a-42f3-892f-76cd9fd6d13c · outbound

This paper cites Self-Consistency Improves Chain of Thought Reasoning in Language Models.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Self-Consistency Improves Chain of Thought Reasoning in Language Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.737780Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f7575ae9-4d0b-4935-8cff-539b7e859031 · outbound

This paper cites Lost in the middle: How language models use long contexts,.

When Does Few-Shot Prompting Help? A Systematic Empirical Study of Shot-Count Effects Across Model Scale, Architecture, and Output Parsing Robustness Lost in the middle: How language models use long contexts,

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-01T04:05:08.797745Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.