Genixer: Empowering Multimodal Large Language Models as a Powerful Data Generator (2312.06731v6)

Abstract: Multimodal LLMs (MLLMs) demonstrate exceptional problem-solving capabilities, but few research studies aim to gauge the ability to generate visual instruction tuning data. This paper proposes to explore the potential of empowering MLLMs to generate data independently without relying on GPT-4. We introduce Genixer, a comprehensive 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.

• The paper introduces a comprehensive four-component pipeline that transforms MLLMs into effective instruction data generators.
• It employs a two-level instruction mechanism along with Fuyu-driven and CLIP-driven filtering to ensure superior data quality.
• Evaluations on benchmarks like VizWiz and ScienceQA demonstrate significant performance boosts, validating Genixer's practical impact.

An Analysis of "Genixer: Empowering Multimodal LLM as a Powerful Data Generator"

The paper introduces Genixer, a data generation pipeline for Multimodal LLMs (MLLMs), aiming to alleviate the challenges associated with creating high-quality instruction tuning data. Traditional methods often rely on expensive models like GPT-4 to generate data; however, they frequently fall short, especially in grounding-based reasoning tasks. Genixer addresses these issues with a novel pipeline and demonstrates the MLLM's potential as a robust data generator.

Core Contributions and Methodology

The authors propose a comprehensive data generation pipeline with four key components:

1. Instruction Data Collection: The paper identifies nine representative multimodal tasks including Common Visual Question Answering (Common VQA), Multi-choice VQA, and Referencing tasks, covering a wide range of data types. These tasks are crucial for exploring the MLLM capabilities in generating diverse instruction tuning data.
2. Instruction Template Design: The introduction of a two-level instruction mechanism allows for controllable data generation. It supports type-agnostic data generation, enabling the model to generate diverse data types without prior constraints, and type-specific data generation, directing the model towards generating specific data types.
3. Empowering MLLMs: By utilizing LLaVA1.5 and Shikra, the authors empower these models, transforming them into data generators. Genixer handles general tasks, while Genixer focuses on grounding tasks. These adaptations demonstrate the MLLM's flexibility in tackling various multimodal instruction datasets.
4. Data Generation and Filtering: The innovative Fuyu-driven and CLIP-driven filtering systems ensure that only high-quality data is used for refining both training and augmentation processes. These methods allow for the rigorous selection of data and underline the importance of quality over quantity.

Quantitative Findings and Results

Through rigorous experimentation, Genixer generated two high-quality instruction tuning datasets: Genixer-915K and Genixer-350K, exemplifying improvements over existing state-of-the-art models such as LLaVA1.5 and Shikra. Significant improvements were recorded on various benchmarks such as VizWiz and ScienceQA, highlighting the efficacy of Genixer-generated data in enhancing model performance.

The paper also conducts an in-depth statistical analysis, human evaluation, and user studies to validate the generated data's quality. The qualitative analysis affirmed Genixer's ability to produce data rivaling that of GPT-4V for several tasks, particularly in generating complex multimodal data types.

Implications and Future Directions

The methodology presented in the paper establishes a pathway for overcoming limitations in instruction tuning data generation, reducing reliance on costly commercial models. The implications of this research are profound, offering an accessible framework for training robust MLLMs capable of complex reasoning across multimodal tasks.

Future developments could expand upon Genixer to explore larger data scales, more varied image sources, and the integration of different LLM architectures to further enhance the diversity and applicability of generated datasets. Moreover, advancements in evaluation techniques, especially for complex data generation tasks, remain a key area for further research.

Conclusion

This paper successfully introduces a comprehensive pipeline designed to empower MLLMs as capable data generators. The structured approach and resulting datasets contribute significantly to the multimodal AI field, presenting a strategic solution to the challenges in data generation for MLLMs. Through its innovative methodologies and potential for scalability, Genixer stands as an essential tool for advancing the practical and theoretical applications of AI in multimodal contexts.