DreamLLM: Synergistic Multimodal Comprehension and Creation
This work addresses the challenge of integrating multimodal understanding and generation for AI systems, representing a novel advancement rather than an incremental improvement.
DreamLLM tackles the problem of limited synergy between multimodal comprehension and creation in Multimodal Large Language Models (MLLMs) by introducing a framework that directly samples in raw multimodal space and generates interleaved documents, achieving superior zero-shot performance as a multimodal generalist.
This paper presents DreamLLM, a learning framework that first achieves versatile Multimodal Large Language Models (MLLMs) empowered with frequently overlooked synergy between multimodal comprehension and creation. DreamLLM operates on two fundamental principles. The first focuses on the generative modeling of both language and image posteriors by direct sampling in the raw multimodal space. This approach circumvents the limitations and information loss inherent to external feature extractors like CLIP, and a more thorough multimodal understanding is obtained. Second, DreamLLM fosters the generation of raw, interleaved documents, modeling both text and image contents, along with unstructured layouts. This allows DreamLLM to learn all conditional, marginal, and joint multimodal distributions effectively. As a result, DreamLLM is the first MLLM capable of generating free-form interleaved content. Comprehensive experiments highlight DreamLLM's superior performance as a zero-shot multimodal generalist, reaping from the enhanced learning synergy. Project page: https://dreamllm.github.io.