DreamLLM: Synergistic Multimodal Comprehension and Creation
Authors: name: Runpei Dong, name: Chunrui Han, name: Yuang Peng, name: Zekun Qi, name: Zheng Ge, name: Jinrong Yang, name: Liang Zhao, name: Jianjian Sun, name: Hongyu Zhou, name: Haoran Wei, name: Xiangwen Kong, name: Xiangyu Zhang, name: Kaisheng Ma, name: Li Yi

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.