arXiv:2502.01015v5 Announce Type: replace Abstract: Task arithmetic, representing downstream tasks through linear operations on task vectors, has emerged as a simple yet powerful paradigm for transferring knowledge across diverse settings. However, maintaining a large collection of task vectors introduces scalability challenges in both storage and computation. We propose Task Vector Bases, a framework compressing $T$ task vectors into $M < T$ basis vectors while preserving the functionality of task arithmetic. By representing each task vector as a structured linear combination of basis atoms,
The proliferation of various AI task vectors creates a growing need for efficient storage and computational methods, making compression techniques increasingly relevant.
This development offers a scalable solution for managing and leveraging diverse AI task knowledge, potentially reducing MLOps costs and enabling more complex AI systems.
The ability to compress and manage task vectors more efficiently changes the practical limitations on how many specialized AI tasks can be simultaneously maintained and deployed.
- · AI model developers
- · Cloud AI providers
- · Large language model companies
- · Inefficient AI storage solutions
- · Hardware providers with limited memory bandwidth
Reduced computational overhead and storage requirements for AI systems that leverage task arithmetic.
Faster development and deployment cycles for AI models by simplifying the management of diverse task knowledge.
Enablement of more complex and specialized multi-task AI agents by removing scalability bottlenecks.
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Read at arXiv cs.LG