AITQE: An Adaptive Image–Text Quality Enhancer for Scalable MLLM Pretraining

Multimodal large language models (MLLMs) have made significant strides by integrating visual and textual modalities. A critical factor in training MLLMs is the quality of image-text pairs within multimodal pretraining datasets. However, in the process of high-quality data curation, filter-based paradigms often discard a substantial portion of high-quality images due to inadequate semantic alignment between images and texts, leading to inefficiency in data utilization and scalability. In this paper, we propose the Adaptive Image-Text Quality Enhancer (AITQE), a model that dynamically assesses and enhances the quality of image-text pairs. AITQE employs a text rewriting mechanism for low-quality pairs and incorporates a negative sample learning strategy to improve evaluative capabilities by integrating deliberately generated low-quality samples during training. Unlike prior approaches that significantly alter text distributions, our method minimally adjusts text to preserve data volume while enhancing quality. Experimental results demonstrate that AITQE surpasses existing methods on various benchmarks, effectively leveraging raw data and scaling with increasing data volumes. Codes and model are available at https://github.com/hanhuang22/AITQE.