arXiv:2602.12005v4 Announce Type: replace Abstract: Language models have consistently grown to compress more world knowledge into their parameters, but the knowledge that can be pretrained into them is upper-bounded by their parameter size. Especially the capacity of Small Language Models (SLMs) is limited, leading to factually incorrect generations. This problem is often mitigated by giving the SLM access to an outside source: the ability to query a larger model, documents, or a database. Under this setting, we study the fundamental question of \emph{which tokens an SLM can and should learn}
The proliferation of increasingly capable Small Language Models (SLMs) and the quest for more efficient and accurate AI necessitate ongoing research into their core learning mechanisms and interaction paradigms.
This research is crucial for optimizing the deployment and effectiveness of SLMs, determining what knowledge should be ingrained vs. dynamically accessed, and fundamentally shaping the architecture of future AI systems.
The focus shifts from simply increasing model size to strategically distributing knowledge between inherent model parameters and external retrieval systems, potentially leading to more specialized and resource-efficient AI applications.
- · Developers of Small Language Models
- · Enterprises deploying edge AI solutions
- · Startups providing external knowledge bases / RAG systems
- · Developers solely focused on ever-larger monolithic models
- · Organizations with inefficient data pipelines for SLM training
Improved performance and cost-efficiency of Small Language Models integrated with external data sources become more common.
This drives innovation in retrieval-augmented generation (RAG) techniques and specialized external knowledge bases for diverse applications.
The compute demands for specific tasks may decrease as SLMs become more adept at leveraging external context, altering cloud infrastructure priorities.
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Read at arXiv cs.CL