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 organizations 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 summarize, 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.
Feature photo by Google DeepMind
