Small Language Models: Architecture, Evolution, and the Future of Artificial Intelligence

Abstract

Large language models (LLMs) have significantly advanced artificial intelligence, yet their high com-putational, energy, and privacy costs pose substantial challenges. In contrast, Small Language Models(SLMs), typically with fewer than 15 billion parameters, have emerged as efficient alternatives. Thissurvey provides a comprehensive analysis of the SLM landscape, tracing their evolution and examiningarchitectural innovations that enhance efficiency. A novel multi-axis taxonomy is introduced to classifySLMs by genesis, architecture, and optimization goals, offering a structured framework for this field.Performance benchmarks are reviewed exhaustively, demonstrating that while LLMs excel in broadknowledge tasks, state-of-the-art SLMs match or exceed larger models in domains such as mathematicalreasoning and code generation. The analysis concludes that the future of AI lies in hybrid ecosystems,where specialized SLMs manage most tasks locally, escalating complex queries to cloud-based LLMs.This tiered approach promises scalability, privacy, and the democratization of AI.

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