Demystifying Small Language Models (SLMs): The Future of Generative Artificial Intelligence

Abstract

Small Language Models (SLMs) are emerging as essential in artificial intelligence (AI), particularly in generative AI and natural language processing (NLP), with optimum efficiency, accessibility, and performance. In generative AI, these models play a crucial role by enabling the creation of high-quality text, code, and content creation (viz., poetry, stories, etc.). While Large Language Models (LLMs) have demonstrated remarkable capabilities in generating human-like language, they are often limited by high computational costs, significant memory necessities, and energy inefficiency. These limitations make LLMs impractical for deployment on edge devices or in low-resource environments, creating a demand for SLMs that provide similar functionalities with optimized efficiency and reduced resource consumption. Unlike LLMs, SLMs are designed to operate with lower computational requirements, fewer parameters, and minimal memory usage, making them ideal for deployment on resource-constrained devices, namely smartphones, IoT devices, and tiny embedded systems. Thus, this paper provides a beginner's guide to SLMs, exploring their core concepts, advantages, and applications.

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