arXiv:2402.14035v4 Announce Type: replace Abstract: Knowledge distillation from foundation models to compact domain models is challenging due to substantial gaps in capacity, architecture, and modality. For example, in our experiments, distilling from a 76M-parameter language model to a 2M-parameter recommender closes less than 40% of the performance gap between the undistilled student and the teacher. We show that introducing domain-specific experts -- which share the student's architectural characteristics -- alongside the foundation model as a diverse teacher committee significantly improve
The proliferation of very large foundation models and the need for efficient, specialized AI applications drive the development of advanced distillation techniques.
This research significantly advances the efficiency and performance of deploying AI in resource-constrained environments by bridging the gap between large foundation models and compact domain-specific models.
The ability to effectively distill expertise from diverse AI 'committees' means more powerful small models can be created, accelerating AI integration into specialized services and devices.
- · AI developers (small models)
- · Edge AI computing
- · Specialized AI applications
- · Domain experts (AI integration)
- · Monolithic foundation model providers (potentially lessened dependency)
- · Companies relying solely on large, inefficient models
Improved performance of compact, domain-specific AI models through more effective knowledge distillation.
Reduced computational costs and energy consumption for AI inference in many applications, broadening AI’s accessibility and deployment.
The proliferation of highly tailored and efficient AI agents across various sectors, leading to a new wave of automation and specialized services.
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Read at arXiv cs.LG