Gemini - A Family of Highly Capable Multimodal Models: Evaluation

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

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.

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