Paper Citation Record · LEDGER

Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2308.13768.

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

pith.paper-citation-record.v1
2308.13768 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-12T15:40:35.869036Z

measured 0 of 1 external citation measurements

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 21528546-2e53-4b39-9989-141ee4e497a8 · inbound

LLM-based Multi-Agent Systems: Techniques and Business Perspectives cites this paper.

LLM-based Multi-Agent Systems: Techniques and Business Perspectives Adversarial Fine-Tuning of Language Models: An Iterative Optimisation Approach for the Generation and Detection of Problematic Content

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-12T15:40:35.869036Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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 2026-08-21T06:32:19.484+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.