DreamLLM: Synergistic Multimodal Comprehension and Creation
Abstract
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.
Community
Hugging face integration planned ?
Hugging face integration planned ?
This is part of our plan. We will release all codes and models with the hugging face implementation in the next one or two months after the paper reviews are out.
My highlights from the paper:
DREAMLLM is a model trained to generate free-form documents with interleaved text and images.
Key points:
- Generating pixels directly retains more visual details vs discrete tokens
- Uses "score distillation" where a diffusion model guides the image training
- Modeling text and images jointly allows full knowledge transfer between modalities
- Introduces "dream queries" to extract multimodal semantics without altering core outputs
In tests, DREAMLLM significantly outperformed other multimodal AI systems at:
- Image captioning
- Answering questions about images
- Assessing image-text relationships
The key insight is that by training AI to create multimodal content, it learns to understand the relationships between vision and language much better. Generation and comprehension abilities reinforce each other synergistically.
I think this shows the value of unified models that connect perception, reasoning, and creation for advancing AI. Overall, it's a small step toward AI that can think more like humans across images, text, and other modalities.
Full summary here. Paper: https://arxiv.org/pdf/2309.11499.pdf
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