A Survey on Small Language Models

ACL ARR 2024 December Submission2297 Authors

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.

Paper Type: Long

Research Area: Efficient/Low-Resource Methods for NLP

Research Area Keywords: small language models, survey, model efficiency

Contribution Types: Surveys

Languages Studied: English

Submission Number: 2297