What are Small Language Models (SLMs)?

About Small Language Models (SLMs):

• SLMs represent a specialized subset within the broader domain of artificial intelligence, specifically tailored for Natural Language Processing (NLP).
• SLMs are AI models designed to process and generate human language. 
• They’re called “small” because they have a relatively small number of parameters compared to large language models (LLMs) like GPT-3.
• SLM is a type of foundation model trained on a smaller dataset compared to LLMs.
• SLMs are characterized by their compact architecture and less computational power.
• This makes them lighter, more efficient, and more convenient for apps that don’t have a ton of computing power or memory.
• SLMs are engineered to efficiently perform specific language tasks, with a degree of efficiency and specificity that distinguishes them from their LLM counterparts.
• They are specialized in specific tasks and built with curated, selective data sources. 

Q1: What is an example of a SLMs?

An SLM may be tuned and adapted to perform specialized conversational tasks. For example: A programming support agent for specific programming languages, libraries, and use cases. A vision model that can interact with radiologists and extract useful knowledge from medical imagery.

Source: TH

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