Paper Citation Record · LEDGER

In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2212.10670.

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

pith.paper-citation-record.v1
2212.10670 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 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 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:26:55.653632Z

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

9
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 351aa1f1-8960-4d65-8190-096a3142cf8d · inbound

A Survey on Knowledge Distillation of Large Language Models cites this paper.

A Survey on Knowledge Distillation of Large Language Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 24

Resolution: verified exact; arxiv_id, observed 2026-05-17T23:31:11.619407Z

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 8f161929-3086-4a8d-b908-e69d41ac0840 · inbound

A Survey on Efficient Inference for Large Language Models cites this paper.

A Survey on Efficient Inference for Large Language Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 118

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T02:39:33.422880Z

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 35abe4ec-a246-4792-bb1b-989113afea1b · inbound

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges cites this paper.

Large Language models for Time Series Analysis: Techniques, Applications, and Challenges In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T15:26:55.653632Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c18673d4-5a6e-4120-8364-9706559b0ca6 · inbound

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes cites this paper.

AgentDistill: Training-Free Agent Distillation with Generalizable MCP Boxes In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 33

Resolution: unresolved; no resolver link, observed 2026-08-07T00:13:38.817872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fe494981-5346-4f18-922e-9b87c1e7a40b · inbound

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models cites this paper.

RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T23:36:12.774802Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0336e708-884b-4566-88c6-c6fa79698974 · inbound

Exploring the Limits of Model Compression in LLMs: A Knowledge Distillation Study on QA Tasks cites this paper.

Exploring the Limits of Model Compression in LLMs: A Knowledge Distillation Study on QA Tasks In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T18:42:07.132490Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eebee6fe-7b64-47c0-8bc0-ea6c43f73e28 · inbound

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization cites this paper.

Can Smaller LLMs do better? Unlocking Cross-Domain Potential through Parameter-Efficient Fine-Tuning for Text Summarization In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.079199Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 472c5697-fbce-427e-a6ea-a73f4fe20b73 · inbound

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning cites this paper.

Large Language Model Post-Training: A Unified View of Off-Policy and On-Policy Learning In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 59

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T00:30:53.891992Z

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 28641ed8-1c1b-47be-99d6-7f1a31a57fd3 · inbound

PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners cites this paper.

PAINT: Partial-Solution Adaptive Interpolated Training for Self-Distilled Reasoners In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-05-09T03:55:08.448617Z

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 d65d82bd-6755-4345-9668-bc5c0cac8e31 · inbound

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models cites this paper.

Chain-based Distillation for Effective Initialization of Variable-Sized Small Language Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 49

Resolution: verified exact; arxiv_id, observed 2026-05-11T03:40:54.046222Z

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 1dec218e-018a-449c-82fd-0b01cbe6fe3b · inbound

XPERT: Expert Knowledge Transfer for Effective Training of Language Models cites this paper.

XPERT: Expert Knowledge Transfer for Effective Training of Language Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 28

Resolution: verified exact; arxiv_id, observed 2026-05-12T02:46:18.779534Z

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 636e47df-8da1-4552-9799-5cbef328143c · inbound

Self-Supervised On-Policy Distillation for Reasoning Language Models cites this paper.

Self-Supervised On-Policy Distillation for Reasoning Language Models In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 41

Resolution: verified exact; arxiv_id, observed 2026-05-20T14:43:21.841999Z

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 77cb0785-5095-4103-b210-b42f5c085244 · inbound

Sample-Efficient Learning from Agent Experience cites this paper.

Sample-Efficient Learning from Agent Experience In-context Learning Distillation: Transferring Few-shot Learning Ability of Pre-trained Language Models

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-01T08:43:21.212188Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.