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The availability and utility of SLMs or Small Language Models are increasing significantly at the same time when LLMs or Large Language Models are developing strongly. While LLMs continue to dominate the AI landscape, a new breed of models is growing rapidly, SLMs. These smaller and more efficient models are gaining popularity because of their versatility, potential to bring revolution in various industries and cost-effectiveness.
Large Language Models (LLMs) such as ChatGPT have shown impressive natural language capabilities by being trained on extensive online datasets. Smaller Language Models (SLMs) aim to deliver the same functionalities but in a compact form that can also operate on smartphones and other devices without the requirements of a continuous internet connection.
Instead of being competitive, LLMs and SLMs can be seen as complementary to each other, they can be taken as complementary solutions that are tailored for several applications.
LLMs do have more extensive knowledge and better capabilities but they require strong servers and stable internet connectivity. Whereas SLMs may have more constraints they can function locally on devices, potentially improving security, speed and offline usability. The current SLMs often submit less detailed responses as compared to LLMs, although this could be improved as technology progresses. There are certain SLMS that provide optional internet connectivity to expand the horizon of their knowledge when essential.
LLMs tend to have a wide range of language mimicry because of the vast and impressive data they have and can discuss, on the other hand, SLMs generally are compacted and have less data they can rely on. LLMs perform on large servers making them quick in responding, while SLMs only rely on memory. Although LLM is currently an important topic, attention needs to be paid to SLM too.