Off-the-shelf models assess quality and alignment to select diverse multimodal training data, letting models trained on the filtered subset match or exceed full-dataset results on standard benchmarks.
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DOSE: Data Selection for Multi-Modal LLMs via Off-the-Shelf Models
Off-the-shelf models assess quality and alignment to select diverse multimodal training data, letting models trained on the filtered subset match or exceed full-dataset results on standard benchmarks.