arXiv:2504.17421v2 Announce Type: replace Abstract: Large language models (LMs) offer broad generalization capabilities but require vast amounts of data and computational resources for domain-specific tasks; small models (SMs), in contrast, are more efficient and tailored to specific domains yet lack general-purpose coverage. Taking a collaborative approach, where large and small models work synergistically, can accelerate the adaptation of LLMs to private domains and unlock new potential in AI. This survey presents a comprehensive overview of recent advances and challenges in harnessing the c
The accelerating development and deployment of large language models are highlighting their computational and data demands, making collaborative approaches with smaller, specialized models a practical necessity for broader domain adoption.
This survey indicates a maturation in AI development strategies, moving beyond a 'bigger is always better' paradigm to more efficient and adaptable hybrid models, crucial for enterprise and closed-domain AI applications.
The focus shifts from purely monolithic large models to architectures that strategically integrate the strengths of both large and small models, enabling more efficient and tailored AI solutions for specific domains.
- · Enterprises with private data
- · Small to medium AI developers
- · Cloud providers with diversified AI offerings
- · Domain-specific AI solution providers
- · AI companies exclusively focused on massive general-purpose models
- · Companies unable to adapt to hybrid model architectures
- · Legacy systems with no AI integration pathways
Increased efficiency and accessibility for specialized AI applications across various industries.
A potential reduction in the computational and data barriers for AI adoption in new domains, fostering a more diverse AI ecosystem.
The acceleration of AI development in 'private' or sensitive domains where data sovereignty and computational constraints are critical, possibly influencing national AI strategies.
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