# A Survey of Small Language Models

**Authors:** Chien Van Nguyen, Xuan Shen, Ryan Aponte, Yu Xia, Samyadeep Basu, Zhengmian Hu, Jian Chen, Mihir Parmar, Sasidhar Kunapuli, Joe Barrow, Junda Wu, Ashish Singh, Yu Wang, Jiuxiang Gu, Franck Dernoncourt, Nesreen K. Ahmed, Nedim Lipka, Ruiyi Zhang, Xiang Chen, Tong Yu  
**arXiv:** [2410.20011](https://arxiv.org/abs/2410.20011)  
**Date:** 2024-10-25  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2410.20011v1/

## Key points

- Small Language Models (SLMs) are increasingly significant in various devices and environments
- Survey covers model architectures, training techniques, and model compression methods for optimizing SLMs
- Introduces innovative taxonomy for evaluating SLMs and discusses their crucial role in different settings and applications
- Emphasizes importance of energy efficiency in SLMs, especially on battery-powered devices to extend battery life
- Privacy concerns related to training data leakage, system prompt misuse, and inference-time data are thoroughly discussed
- Benchmark datasets commonly used for evaluating SLMs are mentioned
- Fundamental challenges in the field of SLMs need to be addressed
- Risks such as hallucination and reinforcement of societal biases persist and require further research efforts

## Summary

This paper provides a comprehensive survey on Small Language Models (SLMs), highlighting their increasing significance in various devices and environments. The survey covers model architectures, training techniques, and model compression methods aimed at optimizing SLMs. It also introduces an innovative taxonomy for evaluating SLMs and discusses their crucial role in different settings and applications. Additionally, the importance of energy efficiency in SLMs is emphasized, particularly when used on battery-powered devices. Studies have shown that concise responses can help extend battery life. Furthermore, privacy concerns related to training data leakage, system prompt misuse, and inference-time data are thoroughly discussed. The survey also touches upon benchmark datasets commonly used for evaluating SLMs and outlines fundamental challenges that need to be addressed in this field. While SLMs offer numerous benefits, risks such as hallucination and reinforcement of societal biases still persist and require further research efforts to mitigate effectively. Overall, this comprehensive survey aims to serve as a valuable resource for researchers and practitioners interested in developing and deploying efficient small language models. By addressing key aspects such as model optimization techniques, evaluation metrics, energy efficiency considerations, privacy concerns, benchmark datasets, and open challenges within the realm of SLMs  this paper sets the stage for driving advancements in compact yet powerful language models.