Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.04401.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-10T23:40:19.205513Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-05T18:12:38.765761Z
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 364ec7c8-97db-4b9f-be13-942158f325aa · inbound
On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs cites this paper.
On the Validity of Traditional Vulnerability Scoring Systems for Adversarial Attacks against LLMs Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Reference 162
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T23:40:19.205513Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e7a9df9f-6b17-49af-a957-e5366528a70c · inbound
Large Language Model Adversarial Landscape Through the Lens of Attack Objectives cites this paper.
Large Language Model Adversarial Landscape Through the Lens of Attack Objectives Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-09T10:29:50.004481Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 1c195647-b3af-43cd-adb7-0ae745e1f4ff · inbound
SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation cites this paper.
SoK: The Privacy Paradox of Large Language Models: Advancements, Privacy Risks, and Mitigation Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Reference 156
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T00:46:10.546530Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 227c6367-f520-45d9-be7c-385f467585e9 · inbound
A Survey on Model Extraction Attacks and Defenses for Large Language Models cites this paper.
A Survey on Model Extraction Attacks and Defenses for Large Language Models Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Reference 91
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:23:15.392554Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 195a96e3-ad03-45d8-8815-45411d2de3ed · inbound
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives cites this paper.
A Systematic Survey of Model Extraction Attacks and Defenses: State-of-the-Art and Perspectives Ethicist: Targeted Training Data Extraction Through Loss Smoothed Soft Prompting and Calibrated Confidence Estimation
Reference 261
- Resolution
- verified exact
- local_arxiv, observed 2026-08-05T18:12:38.771416Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.