What are Small Language Models (SLMs)?

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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