Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.13181.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T21:17:23.273943Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-21T00:33:52.603059Z
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 e106b73b-ea47-42f7-92fd-0ca0b5df5a87 · inbound
Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach cites this paper.
Transferable Modeling Strategies for Low-Resource LLM Tasks: A Prompt and Alignment-Based Approach Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T21:17:23.273943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation fb01821d-e3c0-47e4-99fe-00200da6c475 · inbound
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective cites this paper.
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T20:46:12.503565Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 9f3f9a16-7f98-4c48-bc6e-425257e9a0c8 · inbound
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective cites this paper.
Fine-tuning vs. In-context Learning in Large Language Models: A Formal Language Learning Perspective Comparative Analysis of Different Efficient Fine Tuning Methods of Large Language Models (LLMs) in Low-Resource Setting
Reference 63
- Resolution
- verified exact
- arxiv_id, observed 2026-05-21T00:33:52.604544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.