Paper Citation Record · LEDGER

Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 16 inbound Pith citation observations for arXiv:2205.05638.

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

pith.paper-citation-record.v1
2205.05638 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 16 of 16 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 16 of 16 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:11:29.961290Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:09:46.495658Z

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 0183dc41-e8f0-44ae-af56-2cc866c6845e · inbound

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model cites this paper.

BLOOM: A 176B-Parameter Open-Access Multilingual Language Model Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 93

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T00:51:11.419681Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 517eab86-cc0e-441a-96fa-00b5aeaf9587 · inbound

ART: Automatic multi-step reasoning and tool-use for large language models cites this paper.

ART: Automatic multi-step reasoning and tool-use for large language models Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 168

Resolution: metadata mismatch; arxiv_id, observed 2026-05-16T19:03:06.318529Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 914c1f4b-09f8-4735-a256-554bf54367e4 · inbound

Survey in Characterizing Semantic Change cites this paper.

Survey in Characterizing Semantic Change Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 54

Resolution: verified exact; arxiv_id, observed 2026-05-24T03:53:55.583672Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation ac76dc4c-03f5-4367-99ea-c1bbee681e41 · inbound

Limited-Resource Adapters Are Regularizers, Not Linguists cites this paper.

Limited-Resource Adapters Are Regularizers, Not Linguists Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-07T12:24:05.649864Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 441411b7-2850-41a3-b225-e8d301fcf21f · inbound

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models cites this paper.

From Generation to Generalization: Emergent Few-Shot Learning in Video Diffusion Models Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-07T05:41:31.055832Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1fd94bda-3dc6-41a6-8efe-f443d0c10139 · inbound

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning cites this paper.

15,500 Seconds: Lean UAV Classification Using EfficientNet and Lightweight Fine-Tuning Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-07T15:11:29.961290Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0f65d03e-030e-4191-bd48-1320b3b7205e · inbound

Can Gradient Descent Simulate Prompting? cites this paper.

Can Gradient Descent Simulate Prompting? Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-06T22:41:50.987020Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ffc1ef9-af33-4190-97f3-ab1d7d0112e3 · 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 Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-05T12:43:43.790522Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 91c86a9d-a97f-42bd-a87d-38b9bf2b9d71 · inbound

RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning cites this paper.

RapidUn: Influence-Driven Parameter Reweighting for Efficient Large Language Model Unlearning Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 4

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T17:34:17.386509Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 5d42f028-50b9-4d66-ac81-b8581d8698d8 · inbound

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code cites this paper.

An Initial Exploration of Contrastive Prompt Tuning to Generate Energy-Efficient Code Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 16

Resolution: verified exact; arxiv_id, observed 2026-05-15T16:40:10.543066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 993e34c6-12b1-4303-a6ee-487bc8fb2f64 · inbound

CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters cites this paper.

CoLLM: Continuous Adaptation for SLO-Aware LLM Serving on Shared GPU Clusters Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-05-21T10:34:07.212972Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 82169ca3-2bc5-4a50-b2c1-730984737b82 · inbound

Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation cites this paper.

Parameter Efficiency Is Not Memory Efficiency: Rethinking Fine-Tuning for On-Device LLM Adaptation Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-13T20:38:15.051606Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 26ee190a-ae74-475e-bdc8-1bc2c744f9fc · 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 Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-11T22:26:13.319211Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 2f314c38-17c0-4c41-a2a3-aa4e580d291c · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 113

Resolution: metadata mismatch; arxiv_id, observed 2026-07-04T11:09:46.497250Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

Observation 0c5b0788-e388-4715-8218-c076df57b63c · inbound

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems cites this paper.

The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 103

Resolution: unresolved; no resolver link, observed 2026-08-02T10:27:18.056768Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b97e5972-d4b9-4283-8c72-3f256778053e · inbound

LARA: Lightweight Adapters in the Residual Stream for Composable Adaptation and Alignment cites this paper.

LARA: Lightweight Adapters in the Residual Stream for Composable Adaptation and Alignment Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-03T00:49:10.632805Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.