  In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, 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, we provide a
comprehensive survey aimed at facilitating further research of MM-LLMs.
Initially, we outline general design formulations for model architecture and
training pipeline. Subsequently, we introduce a taxonomy encompassing 126
MM-LLMs, each characterized by its specific formulations. Furthermore, we
review the performance of selected MM-LLMs on mainstream benchmarks and
summarize key training recipes to enhance the potency of MM-LLMs. Finally, 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.
