Benchmarking Large Multimodal Models against Common Corruptions

• URL: http://arxiv.org/abs/2401.11943v1
• Date: Mon, 22 Jan 2024 13:33:53 GMT
• Title: Benchmarking Large Multimodal Models against Common Corruptions
• Authors: Jiawei Zhang, Tianyu Pang, Chao Du, Yi Ren, Bo Li, Min Lin
• Abstract summary: This technical report aims to fill a deficiency in the assessment of large multimodal models (LMMs) We investigate the cross-modal interactions between text, image, and speech, encompassing four essential generation tasks. We create a benchmark, named MMCBench, that covers more than 100 popular LMMs.
• Score: 45.26424202601339
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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