Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
As of 20 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2406.11201.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T17:55:56.949926Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-12T12:16:16.734455Z
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 13355506-c841-4f73-966d-afe6e1eef2ef · inbound
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning cites this paper.
Challenges in Adapting Multilingual LLMs to Low-Resource Languages using LoRA PEFT Tuning Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-12T11:06:13.481831Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.
Observation 9bb5bf3c-0d70-476e-af21-dc6c989e591d · inbound
Studying Disinformation Narratives on Social Media with LLMs and Semantic Similarity cites this paper.
Studying Disinformation Narratives on Social Media with LLMs and Semantic Similarity Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
Reference 5
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T17:55:56.949926Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation ef988659-74f5-4910-be78-244b43053340 · inbound
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go cites this paper.
Go-UT-Bench: A Fine-Tuning Dataset for LLM-Based Unit Test Generation in Go Fine-Tuning or Fine-Failing? Debunking Performance Myths in Large Language Models
Reference 2022
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T22:23:28.850280Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.