Paper Citation Record · LEDGER

Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

As of 12 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 11 inbound Pith citation observations for arXiv:2410.19878.

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

pith.paper-citation-record.v1
2410.19878 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 11 of 11 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 11 of 11 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:12:16.274751Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T12:44:39.957797Z

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 85845222-0365-467a-af45-ef221c675086 · inbound

ComMer: a Framework for Compressing and Merging User Data for Personalization cites this paper.

ComMer: a Framework for Compressing and Merging User Data for Personalization Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-10T22:12:16.274751Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f769793c-5228-45cf-b639-a08568fca2f3 · inbound

Large language models for automated scholarly paper review: A survey cites this paper.

Large language models for automated scholarly paper review: A survey Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-10T19:16:13.025886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5924fe5e-08bd-4284-b986-38cb5124628f · inbound

Parameter-Efficient Fine-Tuning for Foundation Models cites this paper.

Parameter-Efficient Fine-Tuning for Foundation Models Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-10T15:38:02.951973Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cb68dde2-2444-42a3-9549-4070ae9b799c · inbound

Joint Localization and Activation Editing for Low-Resource Fine-Tuning cites this paper.

Joint Localization and Activation Editing for Low-Resource Fine-Tuning Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 45

Resolution: unresolved; no resolver link, observed 2026-08-09T16:20:57.798716Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a2ae0ec1-7fa4-471c-ab8e-07e573144a68 · inbound

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities cites this paper.

Unveiling Instruction-Specific Neurons & Experts: An Analytical Framework for LLM's Instruction-Following Capabilities Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T13:43:23.358774Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 87bd92fd-8797-4bc4-8430-1afcc0191bb7 · inbound

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training cites this paper.

The Art of (Mis)alignment: How Fine-Tuning Methods Effectively Misalign and Realign LLMs in Post-Training Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-11T00:45:50.660773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation edff6a8c-4674-4f00-a191-77c01506d2f7 · inbound

DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation cites this paper.

DriftGuard: Safety-Aware Multi-Monitor Detection and Selective Adaptation for Evolving Toxicity Moderation Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:44:39.959377Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 7a68518f-ad23-4ef1-b412-9e09bc81d1d4 · inbound

Little Brains, Big Feats: Exploring Compact Language Models cites this paper.

Little Brains, Big Feats: Exploring Compact Language Models Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 34

Resolution: verified exact; arxiv_id, observed 2026-06-30T06:24:19.335615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 606f1a5f-4352-4304-828e-39508d45c43a · inbound

Data-Efficient Adaptation of LLMs via Attention Head Reweighting cites this paper.

Data-Efficient Adaptation of LLMs via Attention Head Reweighting Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-02T05:16:37.830928Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4368209-2ed1-4682-ac83-42058e457b84 · inbound

ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation cites this paper.

ZMIS-SAM: Segment Anything Model Enhanced with Wavelet Transform for Zooplankton Microscopy Image Instance Segmentation Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-01T05:13:23.595056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8b875ad6-5590-4349-8c7b-a8afcb801813 · inbound

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs cites this paper.

The Parts Are Greater Than the Sum: Automated Task Sequencing for Efficient Training of Multi-Policy LLMs Parameter-Efficient Fine-Tuning in Large Models: A Survey of Methodologies

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-03T03:50:58.753155Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.