Paper Citation Record · LEDGER

Selective Annotation Makes Language Models Better Few-Shot Learners

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2209.01975.

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

pith.paper-citation-record.v1
2209.01975 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 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 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:44:35.445205Z

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

63
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 a095a952-1a48-4339-934e-59894a9998a3 · inbound

Automatic Chain of Thought Prompting in Large Language Models cites this paper.

Automatic Chain of Thought Prompting in Large Language Models Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 16

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T10:39:17.082652Z

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.

Observation ff90a715-450e-4fd5-8e3f-62c82492fc60 · inbound

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation cites this paper.

AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T15:44:35.445205Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bc57197-ae01-4c83-a78e-3241e8619101 · inbound

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning cites this paper.

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:45.125390Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2c2a308e-755a-48a6-b842-81e0f867e04e · inbound

ConText: Driving In-context Learning for Text Removal and Segmentation cites this paper.

ConText: Driving In-context Learning for Text Removal and Segmentation Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T11:01:11.803292Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbc0822d-c4fd-4810-977a-902a7c829b29 · inbound

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks cites this paper.

Which Prompting Technique Should I Use? An Empirical Investigation of Prompting Techniques for Software Engineering Tasks Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-07T10:19:39.117449Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2ea6d7f7-9ec2-42de-b104-a8576be57fc4 · inbound

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios cites this paper.

Modeling Data Diversity for Joint Instance and Verbalizer Selection in Cold-Start Scenarios Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-06T21:24:43.767154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 62083222-6140-4f4c-b8ac-0bd414f7dfc6 · inbound

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis cites this paper.

Unveiling Effective In-Context Configurations for Image Captioning: An External & Internal Analysis Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:21:13.274079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation baf77153-43fa-4838-9a1a-dfafc3bb4a0f · inbound

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer cites this paper.

DICE: Dynamic In-Context Example Selection in LLM Agents via Efficient Knowledge Transfer Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-06T10:43:50.097303Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e2e71a6f-e9a9-4693-883e-bceac4193056 · inbound

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity cites this paper.

InSQuAD: In-Context Learning for Efficient Retrieval via Submodular Mutual Information to Enforce Quality and Diversity Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T14:44:27.998077Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a5f5990b-6f0a-4d99-a3f8-b55e2af2be92 · inbound

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval cites this paper.

ALLabel: Three-stage Active Learning for LLM-based Entity Recognition using Demonstration Retrieval Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 32

Resolution: unresolved; no resolver link, observed 2026-08-04T22:12:12.693085Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 86668350-48b8-4e63-a3e9-69cef64e2570 · inbound

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences cites this paper.

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 117

Resolution: verified exact; arxiv_id, observed 2026-05-18T16:41:38.026666Z

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.

Observation a6ddc219-4cee-40f7-b4cc-1f4ca3a982b8 · inbound

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences cites this paper.

The Prompt Engineering Report Distilled: Quick Start Guide for Life Sciences Selective Annotation Makes Language Models Better Few-Shot Learners

Reference 118

Resolution: metadata mismatch; arxiv_id, observed 2026-05-18T16:41:37.361423Z

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.