MetaMorph: Multimodal Understanding and Generation via Instruction Tuning (2412.14164v1)

Abstract: In this work, we propose Visual-Predictive Instruction Tuning (VPiT) - a simple and effective extension to visual instruction tuning that enables a pretrained LLM to quickly morph into an unified autoregressive model capable of generating both text and visual tokens. VPiT teaches an LLM to predict discrete text tokens and continuous visual tokens from any input sequence of image and text data curated in an instruction-following format. Our empirical investigation reveals several intriguing properties of VPiT: (1) visual generation ability emerges as a natural byproduct of improved visual understanding, and can be unlocked efficiently with a small amount of generation data; (2) while we find understanding and generation to be mutually beneficial, understanding data contributes to both capabilities more effectively than generation data. Building upon these findings, we train our MetaMorph model and achieve competitive performance on both visual understanding and generation. In visual generation, MetaMorph can leverage the world knowledge and reasoning abilities gained from LLM pretraining, and overcome common failure modes exhibited by other generation models. Our results suggest that LLMs may have strong "prior" vision capabilities that can be efficiently adapted to both visual understanding and generation with a relatively simple instruction tuning process.

• The paper introduces Visual-Predictive Instruction Tuning (VPiT) to enable LLMs to generate both text and visual tokens through unified autoregressive modeling.
• The paper demonstrates that enhanced visual understanding naturally triggers efficient visual generation, requiring minimal generation data.
• The paper finds that increased understanding data significantly boosts model performance compared to generation data, highlighting a crucial data contribution asymmetry.

MetaMorph: Multimodal Understanding and Generation via Instruction Tuning

The paper under discussion introduces an innovative approach to advancing the capabilities of LLMs in multimodal tasks. The authors propose a new technique called Visual-Predictive Instruction Tuning (VPiT), which extends traditional visual instruction tuning. This enables pretrained LLMs to efficiently morph into unified autoregressive models capable of generating both text and visual tokens. The core idea revolves around improving both visual understanding and generation through an efficient instruction tuning process that leverages pretrained language capabilities.

Key Findings

1. Efficient Visual Generation Emergence: One of the noteworthy claims in the paper is that visual generation capabilities emerge naturally as a byproduct of enhanced visual understanding. The paper demonstrates that LLMs can efficiently unlock this ability with a relatively small amount of generation data.
2. Mutually Beneficial Understanding and Generation: The paper finds a symbiotic relationship between understanding and generation capabilities. Specifically, visual understanding substantially contributes to the efficacy of generation, while generation data also enhances understanding but to a lesser extent.
3. Asymmetrical Contribution from Data: Surprisingly, the impact of increasing understanding data is found to be significantly more profound than that of generation data. This asymmetry suggests that understanding tasks may inherently improve the latent vision capabilities of LLMs.
4. Unified Model Performance: Utilizing these insights, the authors developed MetaMorph, a model that demonstrates competitive performance on both visual understanding and generation tasks. Their results show that models can leverage pretrained LLM knowledge and implicit reasoning abilities, solving tasks requiring complex logical deductions without explicit stepwise prompts.

Experimental Observations

• Data Efficiency: Jointly training on visual understanding data requires fewer samples than training solely on generation data, highlighting a path to efficient multimodal model development.
• Scalability Across Models: The synergy between visual understanding and generation was observed across different LLMs, including LLaMA-3.1 and LLaMA-3 70B, reinforcing the robustness of the method.
• Broad Data Utilization: The paper leverages a wide array of data types, extending beyond typical question-answer pairs to include video, transformation, and visual thought propagation tasks.

Implications

The research presents a theoretical shift in the approach to training multimodal LLMs, suggesting that inherent visual knowledge in LLMs can be efficiently unlocked for generation tasks through integrated instruction tuning. Practically, this could lead to the development of more versatile models capable of handling a broader range of tasks with lesser data requirements. It also suggests that future developments in the multimodal field should consider the interplay between different sensory modalities within models.

Future Prospects

Potential directions for future research include exploring the limits of such unified models in even more complex tasks, including those requiring simultaneous inputs from multiple sensory sources. Additionally, further exploration into the implicit reasoning capabilities observed in these models could unveil new methods for enhancing LLM inference in various domains, expanding their utility both theoretically and practically in AI applications.

In conclusion, the paper makes significant contributions to the field of multimodal understanding and generation, offering insights into the natural emergence of these capabilities in LLMs and proposing efficient strategies for their enhancement.