AI RESEARCH
Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)
arXiv CS.CL
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ArXi:2501.05465v2 Announce Type: replace As foundation AI models continue to increase in size, an important question arises - is massive scale the only path forward? This survey of about 160 papers presents a family of Small Language Models (SLMs) in the 1 to 8B parameters range that nstrate smaller models can perform as well, or even outperform large models. We explore task agnostic, general purpose SLMs, task-specific SLMs and techniques to create SLMs that can guide the community to build models while balancing performance, efficiency, scalability and cost.