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Photo by Google DeepMind:
https://www.pexels.com/photo/an-artist-s-illustration-of-artificial-intelligence-ai-this-illustration-depicts-language-models-which-generate-text-it-was-created-by-wes-cockx-as-part-of-the-visualising-ai-project-l-18069696/
While LLMs, or large language models, have played a pivotal
role in the significant growth witnessed by GenAI, they do come with a number
of built-in issues that act as a damper on the universal adoption of the
technology. For one, the fact that LLM necessitates the training of models that
need to take billions and billions of parameters into account, which is
something that requires an enormous amount of investment. This ensures that
only the largest technology companies with untold resources can seriously look
at adopting this technology. Besides, the sheer consumption of energy to run
the servers can prove to be an environmental nightmare.
This is where the move to SLMs or small language models
makes eminent sense. As these need to conform to a much smaller number of
parameters than in the case of LLMs, they are able to run admirably on devices
with lesser processing power, including browsers, edge & IoT devices, and
smartphones. What’s more, the quantum of resources needed to be deployed for
this is way lower.
SLM technology is more
decentralized in that it can be customized to handle precise tasks as well as
datasets. This exposure to much more diverse datasets often makes them much
more efficient than large language models trained on a limited amount of data.
As smaller language models do not have large hardware requirements, these are
usually much cheaper to deploy, encouraging more and more organisations and
individuals to leverage their power. Another great advantage of using SLMs is
the fact that one no longer needs to share one’s sensitive information with
external servers, helping you to have enhanced digital security. As you can
never really fully comprehend the decision making process with regard to LLMs,
there is an ever present trust deficit that does not bode well for the
implementation of that model in a manner that aligns with your objectives.
The widespread adoption of SLM that we see on a daily basis
includes things like smart mail suggestions, grammar and spelling checks, voice
assistants, real time text translations, search engine auto fills, and so on.
This is a testament to the increased use of SLMs in preference to the
conventional LLMs by more and more businesses and enterprises, especially by
those who put a premium on cost, better control over technology, and the
security of sensitive information.
Summary
Though both LLMs and SLMs have played a critical role in
mainstreaming GenAI, the growing popularity of the latter is something that has
been quite discernible for some time now. To summarise, SLMs are growing in
popularity on account of the fact that LLMs require the deployment of large
amounts of resources, which require a substantial investment. Apart from
that, SLMs lend themselves to customization more easily, making them a more
efficient alternative to LLMs. To top it all, SLMs offer better security.
SLMs are increasingly taking over from
LLMs across small businesses and enterprises and this trend is here to stay.
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