In the world of artificial intelligence, bigger is often considered better. Large language models, with their billions of parameters, have dominated the scene with their impressive ability to understand and generate human-like text. With each new version the model gets bigger. GTP-4 is much larger model than GPT-3.5. While larger models are excellent to get us to AGI, they are not always the best choice for the enterprise use cases.
Enterprises have been quick to adopt LLM-powered applications for various tasks, from drafting memos to handling customer support with chatbots. However, as their usage expands, we have begin to see some of the challenges that comes with using LLMs.
The Problem with Large Language Models: LLMs are like big, hungry giants. They need a lot of power and data to work, which can be expensive . Also, sometimes they can make mistakes or be a bit biased – sometimes that can be more expensive than the actual cost of running them (as we have all learnt in recent weeks).
Enter the heroes of our story: Small Language Models (SLMs). These are like the little siblings of LLMs. They don’t need as much food (data) or power to work, which saves money and is better for the planet. They’re also less likely to make mistakes or be biased.
SLMs are like custom-made suits. Businesses can train them on specific data, so they fit just right for what they need. This means they can get more accurate and helpful answers, for the few things they are trained on. Plus, they’re easier to understand and check, so it’s easier to make sure they’re doing the right thing.
Let’s elaborate:
Efficiency & Speed: With fewer parameters, they require less computational power, making them faster and more cost-effective to run. This is particularly beneficial for real-time applications, where speed is of the essence.
Accessibility: Not everyone has access to high-end hardware and not every use case needs to be on the high-end hardware. SLMs, on the other hand can be deployed on a wider range of devices, including smartphones and laptops.
Specialization: While LLMs are designed to be generalists, small language models can be specialized for specific tasks or domains. This focus allows them to perform exceptionally well in their niche, in most cases outperforming LLMs.
Bottom Line:
While large language models continue to make headlines, it is clear that SLMs have a unique set of advantages that make them a better choice in a lot of scenarios. They’re cheaper, more accurate, and easier to use than their big brothers, making them a smart choice for companies looking to make the most of their AI tools. As technology keeps getting better, SLMs are set to play a big role in making AI work better for everyone – I am hoping this year we will get to see some of it in action!