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arXiv · 2401.11943

Benchmarking Large Multimodal Models against Common Corruptions

Jiawei Zhang·Tianyu Pang·Chao Du·Yi Ren·Bo Li·Min Lin
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Abstract

This technical report aims to fill a deficiency in the assessment of large multimodal models (LMMs) by specifically examining the self-consistency of their outputs when subjected to common corruptions. We investigate the cross-modal interactions between text, image, and speech, encompassing four essential generation tasks: text-to-image, image-to-text, text-to-speech, and speech-to-text. We create a comprehensive benchmark, named MMCBench, that covers more than 100 popular LMMs (totally over 150 model checkpoints). A thorough evaluation under common corruptions is critical for practical deployment and facilitates a better understanding of the reliability of cutting-edge LMMs. The benchmarking code is available at https://github.com/sail-sg/MMCBench

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Jiawei Zhang, Tianyu Pang, Chao Du, Yi Ren, Bo Li, Min Lin. 2024-01-22. Benchmarking Large Multimodal Models against Common Corruptions. https://arxiv.org/abs/2401.11943

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