CoMM: A Coherent Interleaved Image-Text Dataset for Multimodal Understanding and Generation

TL;DR

CoMM is a high-quality dataset designed to improve the coherence, consistency, and alignment of interleaved image-text sequences, enabling better multimodal understanding and generation in large language models.

Contribution

The paper introduces CoMM, a novel dataset with a multi-perspective filtering strategy to enhance training data quality for multimodal interleaved content generation.

Findings

CoMM significantly improves MLLMs' in-context learning capabilities.

New evaluation tasks effectively measure interleaved generation abilities.

High-quality dataset enhances coherence and semantic alignment in multimodal outputs.

Abstract

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.