Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Sparse is Enough in Fine-tuning Pre-trained Large Language Models
As of 16 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2312.11875.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-09T19:36:17.848341Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T23:30:51.342745Z
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 e1906722-64d4-49ad-824f-fa9244388ecb · inbound
Sparse Gradient Compression for Fine-Tuning Large Language Models cites this paper.
Sparse Gradient Compression for Fine-Tuning Large Language Models Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T19:36:17.848341Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9b6f2c04-0c80-4605-be26-8b12d05f768a · inbound
GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation cites this paper.
GEM: A Scale-Aware and Distribution-Sensitive Sparse Fine-Tuning Framework for Effective Downstream Adaptation Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reference 33
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T17:33:16.066343Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 18ee8b81-3bc9-43ef-b521-5cb6e68083f5 · inbound
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs cites this paper.
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reference 23
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T23:30:51.347345Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 0d5e775e-ab87-4672-966f-96573f51e73d · inbound
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning cites this paper.
Super-Tuning: From Activation-Aware Pruning to Sparse Fine-Tuning Sparse is Enough in Fine-tuning Pre-trained Large Language Models
Reference 14
- Resolution
- unresolved
- no resolver link, observed 2026-07-13T04:08:39.594367Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.