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.

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.

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

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

In DREAMLLM, the conditional embedding is derived from MLLMs with some learned dream queries.

To elucidate the synergy between multimodal creation and comprehension, we make the comparison among three methods with DREAMLLM architecture

These figures illustrate DREAMLLM’s proficiency in comprehending and generating long-context multimodal information in various input and output formats.

We evaluate the natural language processing capabilities of DREAMLLM post-multimodal adaptation learning via zero-shot experiments

This breakthrough garnered a lot of attention and paved the way for further research and development in the field.

Systemic evaluations of DREAMLLM regarding VL comprehension, content creation, and NLP capabilities have been conducted. See the used benchmarks and datasets