ARC Survey Organizes Data Quality Challenges in Large Vision-Language Models
The survey Data Quality Management for Large Vision–Language Models: Issues, Techniques, and Prospects studies data quality across pre-training, fine-tuning, and inference. It introduces the ARC framework, which organizes issues around Availability, Reliability, and Credibility.
The accompanying public repository provides a taxonomy, a diagnosis workflow, and a curated literature list. The work is presented as a public preprint and living resource, not as a conference acceptance.