DreamLLM: Synergistic Multimodal Comprehension and Creation

Understand

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.