Generating images enhances DREAMLLM's ability to comprehend text, researchers find.

Multimodal signals typically exhibit modality-specific information that has distinct structure but complementary semantics (Dong et al., 2023).

Here's all the background on DreamLLM, a new learning framework.

This paper presents DREAMLLM, a learning framework that first achieves versatile Multimodal Large Language Models empowered with frequently overlooked synergy.

During training, DREAMLLM is trained to predict this token that indicates where an image emerges, and the conditional image synthesis is performed afterward

In this work, we consider a three-stage training procedure: Alignment training, I-GPT training, and Supervised Fine-tuning.

Leveraging the interleaved generative modeling from I-GPT, DREAMLLM can now generate interleaved documents in a free-form manner.

The substantial improvement on LN-COCO underscores DREAMLLM’s superior capability in processing long-context information.

We introduce DREAMLLM, a universal learning framework that facilitates both MLLM’s comprehension and creation capabilities.

DREAMLLM is a versatile multimodal generalist that excels at zero-shot or in-context vision language comprehension and synthesis tasks.

Rapid developments have been witnessed in extending LLMs like LLaMA to multimodal comprehension that enables human interaction with words and visual content.