Paper Citation Record · LEDGER

On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2106.03164.

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

pith.paper-citation-record.v1
2106.03164 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:59:48.516048Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T00:30:51.435671Z

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 3791e928-d4a4-4282-8192-aed15551d24a · inbound

IterIS: Iterative Inference-Solving Alignment for LoRA Merging cites this paper.

IterIS: Iterative Inference-Solving Alignment for LoRA Merging On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-12T15:15:56.195014Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 85074068-af2e-4f1f-a129-5330e3918b53 · inbound

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models cites this paper.

PEFT A2Z: Parameter-Efficient Fine-Tuning Survey for Large Language and Vision Models On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 222

Resolution: unresolved; no resolver link, observed 2026-08-16T11:59:48.516048Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9b7dc751-04cf-4ab2-b7bf-94b97c0ba3a6 · inbound

Less is More: Adaptive Coverage for Synthetic Training Data cites this paper.

Less is More: Adaptive Coverage for Synthetic Training Data On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:51:08.037033Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e27b41e0-bd9f-4078-9b6e-49644f051cda · inbound

CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models cites this paper.

CacheFL: Privacy-Preserving and Efficient Federated Cache Model Fine-Tuning for Vision-Language Models On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T23:17:00.807289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c5db00c2-a399-4e75-8642-c8748ba64bf4 · inbound

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) cites this paper.

Position is Power: System Prompts as a Mechanism of Bias in Large Language Models (LLMs) On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:08.475859Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcfb74f5-afa6-427c-b08b-fdb06a1fec1c · inbound

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures cites this paper.

Acquiring and Adapting Priors for Novel Tasks via Neural Meta-Architectures On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 64

Resolution: unresolved; no resolver link, observed 2026-08-06T19:32:54.188288Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 124575f3-da61-4c13-947d-0850c8fbf9ad · inbound

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models cites this paper.

DP-FedLoRA: Privacy-Enhanced Federated Fine-Tuning for On-Device Large Language Models On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T19:48:47.600822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dc6af0e4-16d6-486a-b603-11a5be917393 · inbound

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge cites this paper.

Beyond End-to-End: Dynamic Chain Optimization for Private LLM Adaptation on the Edge On the Effectiveness of Adapter-based Tuning for Pretrained Language Model Adaptation

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:30:51.437998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.