Paper Citation Record · LEDGER

Learning to Retrieve In-Context Examples for Large Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2307.07164.

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

pith.paper-citation-record.v1
2307.07164 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:09:45.697415Z

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

13
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 e55bab38-4cab-47cd-b151-f51ade0896d6 · inbound

A Comprehensive Overview of Large Language Models cites this paper.

A Comprehensive Overview of Large Language Models Learning to Retrieve In-Context Examples for Large Language Models

Reference 206

Resolution: verified exact; arxiv_id, observed 2026-05-19T20:32:45.604760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation d83fd7da-c730-475b-8840-0bb068098a1b · inbound

Retrieval-Augmented Generation for Large Language Models: A Survey cites this paper.

Retrieval-Augmented Generation for Large Language Models: A Survey Learning to Retrieve In-Context Examples for Large Language Models

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-05-24T05:13:57.098615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7196f702-4762-4644-a97c-c5c50ded82ee · inbound

Retrieval-Augmented Generation for AI-Generated Content: A Survey cites this paper.

Retrieval-Augmented Generation for AI-Generated Content: A Survey Learning to Retrieve In-Context Examples for Large Language Models

Reference 156

Resolution: verified exact; arxiv_id, observed 2026-05-15T13:32:17.412340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 13dd42aa-997d-4531-817b-cfb3b57702e3 · inbound

Vector Retrieval with Similarity and Diversity: How Hard Is It? cites this paper.

Vector Retrieval with Similarity and Diversity: How Hard Is It? Learning to Retrieve In-Context Examples for Large Language Models

Reference 18

Resolution: verified exact; arxiv_id, observed 2026-05-25T08:45:32.804534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c387715b-de31-4ae1-96cf-5de82cedbd8b · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Learning to Retrieve In-Context Examples for Large Language Models

Reference 105

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.771803Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8783880f-34cd-4d53-8ac8-de10203aee2b · inbound

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

MAPLE: Many-Shot Adaptive Pseudo-Labeling for In-Context Learning Learning to Retrieve In-Context Examples for Large Language Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T15:09:45.697415Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8aff6443-3eff-4603-97b3-1de7d1d49158 · inbound

Learning to Select In-Context Demonstration Preferred by Large Language Model cites this paper.

Learning to Select In-Context Demonstration Preferred by Large Language Model Learning to Retrieve In-Context Examples for Large Language Models

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T14:09:25.776006Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0933282c-5372-4107-9d41-23a6ce9fb9bf · inbound

Retrieval Augmented Generation based Large Language Models for Causality Mining cites this paper.

Retrieval Augmented Generation based Large Language Models for Causality Mining Learning to Retrieve In-Context Examples for Large Language Models

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T12:42:44.266766Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 501a2670-797f-4e85-91e8-5a146080751f · inbound

Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework cites this paper.

Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework Learning to Retrieve In-Context Examples for Large Language Models

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-07T10:20:01.350926Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 415d010d-175a-4b09-b9ea-39fd11371efd · inbound

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models cites this paper.

Refract ICL: Rethinking Example Selection in the Era of Million-Token Models Learning to Retrieve In-Context Examples for Large Language Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:56:50.502760Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cf7c07ab-7798-4bcb-a346-59a94428df94 · inbound

Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study cites this paper.

Understanding the Challenges and Opportunities of Generative AI Apps: An Empirical Study Learning to Retrieve In-Context Examples for Large Language Models

Reference 74

Resolution: verified exact; arxiv_id, observed 2026-05-19T08:42:12.480806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7128cb3a-d652-462f-9b52-9734428ecd8b · inbound

True Multimodal In-Context Learning Needs Attention to the Visual Context cites this paper.

True Multimodal In-Context Learning Needs Attention to the Visual Context Learning to Retrieve In-Context Examples for Large Language Models

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T15:29:35.040526Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aedf7469-92f1-4c05-828c-dddd8bc24b2b · inbound

Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval cites this paper.

Towards Effective In-context Cross-domain Knowledge Transfer via Domain-invariant-neurons-based Retrieval Learning to Retrieve In-Context Examples for Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T22:50:49.947424Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0fc81b6a-1e41-404d-bc1a-6a466c4e59a9 · inbound

Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning cites this paper.

Reason Analogically via Cross-domain Prior Knowledge: An Empirical Study of Cross-domain Knowledge Transfer for In-Context Learning Learning to Retrieve In-Context Examples for Large Language Models

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-10T23:10:49.963157Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 2f51f9f6-309d-46f3-81ee-2473e4e755fd · inbound

GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models cites this paper.

GRIP: Feedback-Guided Prompt Retrieval for Large Multimodal Models Learning to Retrieve In-Context Examples for Large Language Models

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-03T11:28:04.240181Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.