arXiv:2606.28551v1 Announce Type: cross Abstract: Building performant Vision-Language Models (VLMs) requires carefully curating large-scale training datasets, yet the community lacks systematic benchmarks for evaluating such curation strategies. We introduce DataComp for VLMs (DCVLM), a benchmark for controlled data-centric experiments to improve VLM training. As part of DCVLM, we collect 160 datasets spanning four data types -- image-caption pairs, multimodal interleaved documents, text-only, and instruction-tuning data -- into a corpus of 6T multimodal tokens. DCVLM allows participants to te
The rapid advancement and deployment of Vision-Language Models necessitate better data curation strategies to achieve optimal performance and mitigate biases.
Improved, systematically benchmarked datasets are critical for developing more robust and reliable AI systems, directly impacting the capabilities of future AI applications.
The introduction of DataComp-VLM provides a standardized benchmark and a large, diverse dataset collection for evaluating VLM data curation, offering a path to more efficient and effective model training.
- · AI researchers and developers
- · Companies building VLMs
- · AI ethics and safety organizations
- · Open-source AI community
- · Companies with proprietary, unbenchmarked datasets
- · Inefficient VLM training methodologies
VLMs trained on DataComp-VLM will likely exhibit improved performance and generalization capabilities.
Standardized data benchmarks could accelerate innovation in VLM architectures and training techniques.
More capable and reliable VLMs could enable new applications across various industries, from autonomous systems to advanced content creation.
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