Boosting Productivity with Small Language Models

Today's issue is delivered by Krystian Bergmann , AI Consulting Lead at Netguru.?

In 2024, industry giants IBM and Microsoft have identified small language models as one of the most significant trends in artificial intelligence.

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What is a small language model???

If you've ever utilized Copilot to tackle intricate queries, you've witnessed the prowess of large language models. These models demand substantial computing resources to operate efficiently, making the emergence of small language models a significant breakthrough.?

SLMs, though still sizable with several billion parameters, stand in stark contrast to the hundreds of billions found in LLMs. They possess a crucial advantage: they are compact enough to run seamlessly on a smartphone, even offline.?

Parameters serve as the adjustable elements within a model, dictating its behavior and functionality. “Small language models can make AI more accessible due to their size and affordability,” says Sebastien Bubeck, who leads the Machine Learning Foundations group at Microsoft Research. “At the same time, we’re discovering new ways to make them as powerful as large language models.”?

Looking at the market, I expect to see new, improved models this year that will speed up research and innovation.

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What are the benefits of SLMs??

One of the most significant advantages lies in their streamlined design, characterized by fewer parameters and reduced requirements for training. While LLMs may demand hours, if not days, for training, SLMs can be ready for deployment in mere minutes to a few hours. This efficiency not only accelerates the implementation but also makes SLMs more feasible for on-site or smaller device applications.

The flexibility of SLMs allows for customization to cater to specific and niche applications. Unlike LLMs, which often require extensive datasets, SLMs can excel in scenarios where training data is limited. This adaptability makes them particularly appealing for companies seeking language models optimized for specialized domains or industries, where precision is needed.?

SLMs also improve data security, addressing increasing concerns about data privacy and protection. Their smaller codebases and reduced parameter count mean they're less vulnerable to attacks. This stronger security helps businesses better control their AI systems, protect sensitive information, and enhance their overall cybersecurity.

From a financial perspective, SLMs offer a compelling, cost-effective solution for organizations looking to leverage AI capabilities. By minimizing infrastructure costs and resource demands, SLMs help businesses manage expenses while adhering to resource constraints. This affordability enhances the appeal of SLMs as a practical and sustainable choice for integrating AI technologies into business operations.?

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And how can you use them??

Web browsers?

Within web applications, these SLMs can enhance the user experience by providing language-related functions such as auto-completion when typing, grammar correction, and sentiment analysis, which has the capability of spotting emotionally saturated terms and suggesting alternatives. For example, instead of going with a blatant “Just do it..", it could suggest an alternate response, like "Maybe you would consider…”.?

IoT devices?

In IoT devices, small language models enable functions like voice recognition, natural language processing, and personalized assistance without heavy reliance on cloud services. This optimizes both performance and privacy. Right now, Alexa and other home devices have to consult with international servers to turn your Smart Lights or IoT devices on and off. That should be entirely local, with no outside consultation, and now it can be.?

Edge computing?

Small language models shine in edge computing environments, where data processing occurs virtually at the data source. Deployed on edge devices such as routers, gateways, or edge servers, they can execute language-related tasks in real time. This setup lowers delay and reduces reliance on central servers, improving cost-efficiency and responsiveness. ?

With ongoing advancements in training techniques and architectural enhancements, small language models are set to greatly improve in capabilities. As their functionality increases, SLMs are expected to become more integrated into everyday technology.??

Best,

Krystian