Redefining Natural Language Processing Efficiency: The Rise of Small Language Models

Abstract

The rapid advancement of Natural Language Processing (NLP) has been driven by large language models (LLMs), but their extensive computational and memory requirements pose significant challenges. Small Language Models (SLMs) are emerging as an efficient alternative, offering competitive performance with reduced resource demands. This paper explores the architecture, training techniques, and optimization strategies that enable SLMs to achieve remarkable efficiency. It reviews key breakthroughs, including knowledge distillation, parameter pruning, and quantization, which contribute to their lightweight design. Additionally, the paper highlights practical applications where SLMs outperform larger models in terms of speed, adaptability, and deployment feasibility, particularly in resource-constrained environments. The analysis aims to present SLMs as a promising direction for sustainable, accessible, and effective NLP solutions