arXiv:2606.25237v1 Announce Type: cross Abstract: We develop accurate and efficient solutions for large-scale retrieval tasks where novel (zero-shot) items can arrive continuously at a rapid pace. Conventional Siamese-style approaches embed both queries and items through a small encoder and retrieve the items lying closest to the query. While this approach allows efficient addition and retrieval of novel items, the small encoder lacks sufficient capacity for the necessary world knowledge in complex retrieval tasks. The extreme classification approaches have addressed this by learning a separat
The rapid expansion of AI models and data necessitates more efficient and accurate retrieval methods, particularly for novel information, driving innovation in zero-shot learning.
This development can significantly enhance the capability of large-scale AI systems to continuously integrate new data, improving real-time relevance and reducing computational overhead.
The ability to perform accurate zero-shot retrieval at scale suggests a shift towards more adaptable and less training-intensive AI systems for dynamic information environments.
- · AI platform providers
- · E-commerce platforms
- · Search engine companies
- · Data-intensive industries
- · Companies reliant on static, pre-trained retrieval models
- · Small-scale data providers without adaptive systems
Improved efficiency and accuracy in handling novel queries and items in large information systems.
Accelerated development of AI applications that require continuous learning and up-to-date knowledge without constant retraining.
Enhanced ability for AI agents to operate effectively in rapidly changing environments, potentially leading to more sophisticated autonomous systems.
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Read at arXiv cs.LG