Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2309.13256.
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 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T04:28:00.855741Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T16:24:32.319388Z
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 7474bc3b-5c64-4126-9d19-0dc0b36c7995 · inbound
A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations cites this paper.
A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks, Defenses, and Evaluations Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Reference 200
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T00:50:00.778382Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47eaaea1-98e8-4399-8c1d-25a957b302c3 · inbound
LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures cites this paper.
LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Reference 151
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T04:28:00.855741Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d9303fa6-3829-411c-9172-bd0e48dd209e · inbound
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Defending Pre-trained Language Models as Few-shot Learners against Backdoor Attacks
Reference 184
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T16:24:32.350762Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.