The document describes the development of MMIE, a large-scale benchmark designed to evaluate the performance of Large Vision-Language Models (LVLMs) in interleaved multimodal comprehension and generation tasks. MMIE comprises a dataset of 20,000 meticulously curated multimodal queries across various domains, including mathematics, coding, and literature, which are designed to challenge LVLMs to produce and interpret both images and text in arbitrary sequences. The authors also propose a reliable automated evaluation metric for MMIE, leveraging a scoring model fine-tuned with human-annotated data and systematic evaluation criteria. Extensive experiments demonstrate the effectiveness of the benchmark and metrics, revealing significant room for improvement in the development of interleaved LVLMs. The paper provides detailed insights into the benchmark's construction, evaluation methods, and error analysis, offering valuable guidance for future research in multimodal learning.

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