  Interleaved image-text generation has emerged as a crucial multimodal task,
aiming at creating sequences of interleaved visual and textual content given a
query. Despite notable advancements in recent multimodal large language models
(MLLMs), generating integrated image-text sequences that exhibit narrative
coherence and entity and style consistency remains challenging due to poor
training data quality. To address this gap, we introduce CoMM, a high-quality
Coherent interleaved image-text MultiModal dataset designed to enhance the
coherence, consistency, and alignment of generated multimodal content.
Initially, CoMM harnesses raw data from diverse sources, focusing on
instructional content and visual storytelling, establishing a foundation for
coherent and consistent content. To further refine the data quality, we devise
a multi-perspective filter strategy that leverages advanced pre-trained models
to ensure the development of sentences, consistency of inserted images, and
semantic alignment between them. Various quality evaluation metrics are
designed to prove the high quality of the filtered dataset. Meanwhile,
extensive few-shot experiments on various downstream tasks demonstrate CoMM's
effectiveness in significantly enhancing the in-context learning capabilities
of MLLMs. Moreover, we propose four new tasks to evaluate MLLMs' interleaved
generation abilities, supported by a comprehensive evaluation framework. We
believe CoMM opens a new avenue for advanced MLLMs with superior multimodal
in-context learning and understanding ability.
