A Survey of Small Language Models

TL;DR

This survey comprehensively reviews Small Language Models, detailing their architectures, optimization methods, benchmarking datasets, and open challenges to guide future research and deployment in resource-constrained environments.

Contribution

Introduces a novel taxonomy for categorizing optimization techniques for SLMs and consolidates benchmark datasets and evaluation metrics.

Findings

Summarizes key architectures and training techniques for SLMs.

Provides a taxonomy for model optimization methods.

Highlights open challenges in deploying SLMs.

Abstract

Small Language Models (SLMs) have become increasingly important due to their efficiency and performance to perform various language tasks with minimal computational resources, making them ideal for various settings including on-device, mobile, edge devices, among many others. In this article, we present a comprehensive survey on SLMs, focusing on their architectures, training techniques, and model compression techniques. We propose a novel taxonomy for categorizing the methods used to optimize SLMs, including model compression, pruning, and quantization techniques. We summarize the benchmark datasets that are useful for benchmarking SLMs along with the evaluation metrics commonly used. Additionally, we highlight key open challenges that remain to be addressed. Our survey aims to serve as a valuable resource for researchers and practitioners interested in developing and deploying small yet efficient language models.