  We propose a novel framework for filtering image-text data by leveraging
fine-tuned Multimodal Language Models (MLMs). Our approach outperforms
predominant filtering methods (e.g., CLIPScore) via integrating the recent
advances in MLMs. We design four distinct yet complementary metrics to
holistically measure the quality of image-text data. A new pipeline is
established to construct high-quality instruction data for fine-tuning MLMs as
data filters. Comparing with CLIPScore, our MLM filters produce more precise
and comprehensive scores that directly improve the quality of filtered data and
boost the performance of pre-trained models. We achieve significant
improvements over CLIPScore on popular foundation models (i.e., CLIP and BLIP2)
and various downstream tasks. Our MLM filter can generalize to different models
and tasks, and be used as a drop-in replacement for CLIPScore. An additional
ablation study is provided to verify our design choices for the MLM filter.
