Recent advancements, especially with models like LLaMA, highlight the effective integration of LLMs for multimodal comprehension, bridging interactions between text and visuals.
The rise of plugin frameworks has shown considerable success in linking LLMs to various agents, where language serves as a universal interface for communication.
Through methods like parameter-efficient and instruction tuning, LLMs have been increasingly adept at processing and understanding multimodal input, enhancing their utility.
DreamLLM proposes utilizing End-to-End Interleaved Generative Pretraining (I-GPT) to merge creation and comprehension, advancing the state of multimodal generative models.
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