Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2308.13768.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:28:17.670309Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T12:35:24.930430Z
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 24a3a16c-7ad3-457e-9e14-52f68f6040bb · inbound
System Prompt Extraction Attacks and Defenses in Large Language Models cites this paper.
System Prompt Extraction Attacks and Defenses in Large Language Models Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
Reference 25
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:28:17.670309Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9939c42-9503-494e-b4d1-08d081a71d6c · inbound
Adversarial Preference Learning for Robust LLM Alignment cites this paper.
Adversarial Preference Learning for Robust LLM Alignment Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
Reference 29
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T12:35:25.026989Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.
Observation 8705ad8d-9cd4-466b-b034-18c9e58deb4f · inbound
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM cites this paper.
Guardians and Offenders: A Survey on Harmful Content Generation and Safety Mitigation of LLM Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content
Reference 50
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T23:13:03.657036Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.