Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Word-Level Explanations for Analyzing Bias in Text-to-Image Models
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.05500.
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 2026-08-15T18:52:57.453542Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-04T21:25:28.137278Z
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 283459c9-e1ae-4b05-9d75-947c317c3810 · inbound
Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models? cites this paper.
Do Existing Testing Tools Really Uncover Gender Bias in Text-to-Image Models? Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Reference 47
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T14:01:04.632784Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 35125cbd-eec4-49a7-bf3e-66472725cd23 · inbound
Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions cites this paper.
Can we Debias Social Stereotypes in AI-Generated Images? Examining Text-to-Image Outputs and User Perceptions Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Reference 34
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:52:51.300393Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation a523d232-b6a8-42f2-a1ca-b6fe34cb1f5d · inbound
How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models cites this paper.
How Robust is Model Editing after Fine-Tuning? An Empirical Study on Text-to-Image Diffusion Models Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T18:52:57.453542Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 02170d03-b9a9-48b0-9946-3de063dc4f0b · inbound
Effectively obtaining acoustic, visual and textual data from videos cites this paper.
Effectively obtaining acoustic, visual and textual data from videos Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Reference 63
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T05:01:36.805782Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d24a96c5-b7d6-4c71-ab90-f8bde6247612 · inbound
Testing chatbots on the creation of encoders for audio conditioned image generation cites this paper.
Testing chatbots on the creation of encoders for audio conditioned image generation Word-Level Explanations for Analyzing Bias in Text-to-Image Models
Reference 56
- Resolution
- verified exact
- local_arxiv, observed 2026-08-04T21:25:28.146878Z
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.