Compositional Generative Modeling: A Single Model is Not All You Need
(2402.01103)Abstract
Large monolithic generative models trained on massive amounts of data have become an increasingly dominant approach in AI research. In this paper, we argue that we should instead construct large generative systems by composing smaller generative models together. We show how such a compositional generative approach enables us to learn distributions in a more data-efficient manner, enabling generalization to parts of the data distribution unseen at training time. We further show how this enables us to program and construct new generative models for tasks completely unseen at training. Finally, we show that in many cases, we can discover separate compositional components from data.
Overview
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The paper challenges the current trend towards large, monolithic AI models by proposing a shift to compositional generative modeling, which uses smaller, specialized models for increased efficiency and adaptability.
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Compositional generative modeling is presented as a method that improves data efficiency, generalization to new tasks, and allows dynamic adaptation by focusing on specialized subsets of a problem.
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Empirical studies show that compositional models require less data and adapt better to new tasks than monolithic models in various domains, including visual synthesis and trajectory generation.
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Future research directions include optimizing model composition, enhancing the discovery of compositional elements, and applying these models in real-world settings for broader AI applications.
Compositional Generative Modeling Challenges the Primacy of Monolithic AI Models
Introduction
The prevailing trend in artificial intelligence research towards ever-larger monolithic generative models, while marking significant advancements, encounters critical limitations in data efficiency, generalization, and adaptability. Yilun Du and Leslie Kaelbling's paper addresses these challenges and proposes an alternative paradigm centered on compositional generative modeling. By breaking down complex models into simpler, inter-operable components, this approach introduces efficiency, flexibility, and profound implications for future AI model development.
Compositional Generative Modeling Explained
At its core, compositional generative modeling advocates for constructing complex systems as assemblages of smaller, specialized models. Each component model focuses on a subset of the problem space, offering several advantages over the conventional monolithic approach:
- Data Efficiency and Generalization: By training on more focused datasets, compositional models achieve higher data efficiency and can generalize better to new, unseen data distributions.
- Adaptability: This modular structure allows for the dynamic adaptation and recombination of models to tackle new tasks without extensive retraining.
- Discovery of Compositional Components: Components can be identified and extracted directly from data, enabling models to learn and represent discrete elements of the problem space organically.
Key Results
The paper substantiates its claims through empirical studies across various domains, from visual and image synthesis to decision-making and trajectory dynamics. It demonstrates that compositional models not only require less data to achieve comparable or superior performance to monolithic models but also adapt more readily to new tasks. For instance, in trajectory generation and visual synthesis tasks, compositional models displayed remarkable adeptness in leveraging sparse data and complex task instructions, showcasing a superior grasp of the underlying structures and relationships.
Theoretical and Practical Implications
The adoption of compositional generative modeling carries significant implications:
- Theoretical Underpinnings: The compositional approach challenges current understandings of model scalability and efficiency, suggesting that complexity in AI models does not necessarily entail monolithicity.
- Practical Deployability: Modular models offer practical advantages in deployment, including lower computational and financial costs, and increased interpretability and maintainability.
Future Directions
The paper outlines clear trajectories for further research, notably in optimizing the processes for model composition, enhancing the automated discovery of compositional elements, and refining the use of compositional models in dynamic, real-world settings. The pursuit of these avenues promises not only to broaden the applications of compositional generative modeling but also to redefine the boundaries of what is achievable in artificial intelligence research.
Conclusion
Yilun Du and Leslie Kaelbling's exploration of compositional generative modeling provides a compelling argument for reevaluating the current trajectory of AI model development. By advocating for a strategy that prioritizes modularity, specificity, and reconfigurability, the paper lays the groundwork for a future in which AI systems are not only more efficient and adaptable but also inherently more aligned with the complex, componentized nature of real-world phenomena.