Paper Citation Record · LEDGER

CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2403.04547.

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

pith.paper-citation-record.v1
2403.04547 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 2026-08-10T06:31:04.303077+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:53:07.187034Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-20T19:18:54.597138Z

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 3d529ef3-944f-473e-a98e-3cd52142b82f · inbound

Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads cites this paper.

Debiasing CLIP: Interpreting and Correcting Bias in Attention Heads CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-07T14:53:07.187034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2b9a5aa2-af11-4bd9-88a6-8a4adfe96333 · inbound

Hierarchical Pre-Training of Vision Encoders with Large Language Model cites this paper.

Hierarchical Pre-Training of Vision Encoders with Large Language Model CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-04T05:37:50.116321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5b3c6f1d-e2ac-49ce-b5f8-73d093a8f13d · inbound

Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding cites this paper.

Concrete Jungle: Towards Concreteness Paved Contrastive Negative Mining for Compositional Understanding CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-11T10:36:04.892368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9c3aaf54-b46c-499a-9af3-5169687a6017 · inbound

Neutral-Reference Prompting for Vision-Language Models cites this paper.

Neutral-Reference Prompting for Vision-Language Models CLIP the Bias: How Useful is Balancing Data in Multimodal Learning?

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-20T19:18:54.600623Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.