  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
