  The human ability to easily solve multimodal tasks in context (i.e., with
only a few demonstrations or simple instructions), is what current multimodal
systems have largely struggled to imitate. In this work, we demonstrate that
the task-agnostic in-context learning capabilities of large multimodal models
can be significantly enhanced by effective scaling-up. We introduce Emu2, a
generative multimodal model with 37 billion parameters, trained on large-scale
multimodal sequences with a unified autoregressive objective. Emu2 exhibits
strong multimodal in-context learning abilities, even emerging to solve tasks
that require on-the-fly reasoning, such as visual prompting and object-grounded
generation. The model sets a new record on multiple multimodal understanding
tasks in few-shot settings. When instruction-tuned to follow specific
instructions, Emu2 further achieves new state-of-the-art on challenging tasks
such as question answering benchmarks for large multimodal models and
open-ended subject-driven generation. These achievements demonstrate that Emu2
can serve as a base model and general-purpose interface for a wide range of
multimodal tasks. Code and models are publicly available to facilitate future
research.
