The Rise of Small Language Models
Qin Zhang, Ziqi Liu, Shirui Pan · IEEE Intelligent Systems · 2025
Large language models (LLMs), such as GPT and LLAMA, exhibit exceptional comprehension and reasoning capabilities across a wide range of tasks, which are a result of the extensive corpora and the enormous number of parameters in a model. However, their size can pose significant challenges for deployment, particularly on resource-constrained devices. For issues that degrade the user experience, such as efficiency, latency, safety, and privacy, small language models (SLMs) offer a solution. This article begins by outlining the key principles behind SLMs and the reasons for their importance in the field. Subsequently, we discuss the methods used to develop SLMs and explore the collaboration between SLMs and LLMs. By exploring the pathways for harnessing the unique capabilities of SLMs and optimizing their integration with LLMs, it contributes to the ongoing discussion on their application and collaboration in natural language processing and offers insights for advancement and innovation in the field.