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.

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

In this paper, we present DREAMLLM, a comprehensive framework for developing MLLMs that not only understands but also creates multimodal content

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

While DREAMLLM has made significant strides toward the development of versatile, creative, and foundational MLLMs, it still has several limitations.

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