A Small Language Model (SLM) is a type of artificial intelligence model designed to understand and generate human language. It operates on the same principles as Large Language Models (LLMs), but is significantly smaller in scale. This reduced size means SLMs require fewer computational resources, making them more accessible and cost-effective for certain applications. While they may not have the vast knowledge base or the same level of complexity as their larger counterparts, SLMs can still perform a wide range of language-related tasks effectively. They are particularly useful for specialized applications where a full-scale LLM would be unnecessary or impractical. For instance, an SLM could power a chatbot for a specific domain or assist with language processing in devices with limited processing capabilities.
SLMs are particularly useful in applications where resources are limited, such as on mobile devices or in real-time systems. Despite their smaller size, they can still perform well on specific tasks like text classification, sentiment analysis, and simple chatbots.
The development of SLMs is driven by the need for faster inference times and lower energy consumption while maintaining a satisfactory level of accuracy. Researchers often fine-tune these models on specific datasets to improve their performance on targeted applications.
In recent years, advancements in techniques like knowledge distillation and quantization have contributed to the effectiveness of SLMs, allowing them to retain useful capabilities while being lightweight. As a result, SLMs are becoming increasingly popular in various domains, from personal assistive technologies to automated customer support systems.