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 have undergone substantial advancements%2C augmenting off-the-shelf LLMs to support MM inputs or outputs via cost-effective training strategies. The resulting models not only preserve the inherent reasoning and decision-making capabilities of LLMs but also empower a diverse range of MM tasks. In this paper%2C we provide a comprehensive survey aimed at facilitating further research of MM-LLMs. Initially%2C we outline general design formulations for model architecture and training pipeline. Subsequently%2C we introduce a taxonomy encompassing 126 MM-LLMs%2C each characterized by its specific formulations. Furthermore%2C we review the performance of selected MM-LLMs on mainstream benchmarks and summarize key training recipes to enhance the potency of MM-LLMs. Finally%2C we explore promising directions for MM-LLMs while concurrently maintaining a real-time tracking website for the latest developments in the field. We hope that this survey contributes to the ongoing advancement of the MM-LLMs domain.?quality=80&w=800)
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