Paper Citation Record · LEDGER

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

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 25 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 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 25 of 25 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T19:07:30.085698Z

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
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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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+00:00.

Observation 7e3d7adf-2062-4368-8775-9ef333757c58 · inbound

SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization cites this paper.

SymDPO: Boosting In-Context Learning of Large Multimodal Models with Symbol Demonstration Direct Preference Optimization Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T19:07:30.085698Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb845c9-52cb-4241-ba83-03c8a5b51686 · inbound

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education cites this paper.

Scaffold or Crutch? Examining College Students' Use and Views of Generative AI Tools for STEM Education Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-11T23:16:45.168003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebefeda1-43b5-4cb4-b302-e551eaec835b · inbound

LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs cites this paper.

LLMsAgainstHate @ NLU of Devanagari Script Languages 2025: Hate Speech Detection and Target Identification in Devanagari Languages via Parameter Efficient Fine-Tuning of LLMs Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-11T05:48:42.347234Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9210e0d-bcb3-408a-8849-0992e6700c3c · inbound

Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding cites this paper.

Practical Design and Benchmarking of Generative AI Applications for Surgical Billing and Coding Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-10T21:48:15.174538Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c6d2fe52-1b51-4fe7-bde2-3294377bbf3b · inbound

Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures cites this paper.

Atleus: Accelerating Transformers on the Edge Enabled by 3D Heterogeneous Manycore Architectures Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-10T19:57:25.567549Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 171cb759-9610-49d3-a757-3c2bf87fd736 · inbound

Domain Expansion: Parameter-Efficient Modules as Building Blocks for Composite Domains cites this paper.

Domain Expansion: Parameter-Efficient Modules as Building Blocks for Composite Domains Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-10T15:18:07.175376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 84c5de17-5bc3-46c1-bb6a-148ae924c2c1 · inbound

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment cites this paper.

PARA: Parameter-Efficient Fine-tuning with Prompt Aware Representation Adjustment Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-09T16:56:09.472841Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a0b866db-a1c4-4d77-b002-8242eb92c05f · inbound

The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models cites this paper.

The Geometry of Prompting: Unveiling Distinct Mechanisms of Task Adaptation in Language Models Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T11:11:42.731430Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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 2026-08-14T06:32:32.682623+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.

Observation 91488c25-d831-41dd-938f-b4306befc0b7 · inbound

Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models cites this paper.

Router Sensitivity Under Lightweight Fine-Tuning Identifies Prunable Experts in Mixture-of-Experts Models Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-12T00:49:01.952312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.