  Multimodal Large Language Models (MLLMs) demonstrate exceptional
problem-solving capabilities, but there is limited research focusing on their
ability to generate data by converting unlabeled images into visual instruction
tuning data. To this end, this paper is the first to explore the potential of
empowering MLLM to generate data rather than prompting GPT-4. We introduce
Genixer, a holistic data generation pipeline consisting of four key steps: (i)
instruction data collection, (ii) instruction template design, (iii) empowering
MLLMs, and (iv) data generation and filtering. Additionally, we outline two
modes of data generation: task-agnostic and task-specific, enabling
controllable output. We demonstrate that a synthetic VQA-like dataset trained
with LLaVA1.5 enhances performance on 10 out of 12 multimodal benchmarks.
Additionally, the grounding MLLM Shikra, when trained with a REC-like synthetic
dataset, shows improvements on 7 out of 8 REC datasets. Through experiments and
synthetic data analysis, our findings are: (1) current MLLMs can serve as
robust data generators without assistance from GPT-4V; (2) MLLMs trained with
task-specific datasets can surpass GPT-4V in generating complex instruction
tuning data; (3) synthetic datasets enhance performance across various
multimodal benchmarks and help mitigate model hallucinations. The data, code,
and models can be found at https://github.com/zhaohengyuan1/Genixer.
