Small language models (SLMs), algorithms designed to understand and generate human text, are the latest trend in the artificial intelligence (AI) market. These models, a subset of natural language processing (NLP) models, are fast and require less computational power compared to large language models (LLMs). This makes them ideal for smaller companies with limited IT resources. As an alternative to LLMs, SLMs are gaining attention from tech giants like Microsoft, OpenAI, Google, and Meta, as well as smaller tech companies and startups investing in this technology.

Complex Architecture

SLMs are built to handle language-related tasks, but they are significantly smaller in size and computational demand compared to large language models like GPT-3 or BERT. Large language models (LLMs) are equipped with billions of parameters, enabling them to analyze vast datasets and perform a wide range of complex tasks. However, their complexity and cost make them less feasible for all companies. This is where SLMs present a more affordable alternative, particularly for startups and small to medium-sized businesses that cannot afford the high price of LLMs.

Despite their smaller size, SLMs have a complex architecture and are trained on trillions of tokens. One of the key differences between SLMs and LLMs is their size and speed. For instance, while LLMs can take up hundreds of gigabytes, many SLMs are under five gigabytes. SLMs are becoming more popular due to their efficiency, versatility, and diverse applications in business. They can handle various natural language processing tasks such as translation, text summarization, and supporting interactive chatbots, making them useful in many digital applications.

Learn more about how SLMs are changing industries in our AI Trends article.

Easier to Implement

Working with fewer parameters allows SLMs to be easily deployed across various platforms, from smartphones to cloud servers. This makes them ideal for real-time data processing, where quick response times are critical. Moreover, SLMs are more accessible to companies without large hardware resources or budgets for costly technological solutions. The development of SLMs, such as Microsoft’s Phi2, arose from the need to make advanced AI capabilities more affordable and efficient for smaller businesses.

SLMs are commonly used in applications like chatbots, virtual assistants, and text analytics tools. Their reduced parameters and simpler structures allow for faster training and more efficient operation. This makes them ideal for mobile devices and smaller servers, reducing both operational costs and energy consumption.

Explore how AI solutions like SLMs are implemented in our Guide to Implementing AI Solutions.

Industry Use Cases

For example, Phi-2 is designed for mobile applications, converting speech to text and translating in real-time on smartphones. Healthcare uses SLMs to improve patient care by processing and analyzing clinical notes and patient feedback in real-time, enabling more personalized care and timely interventions. Banks and financial institutions also use SLMs to enhance customer service and automate routine queries, reducing call center volumes by as much as 40%.

SLMs allow companies to implement sophisticated AI tools without significant investment, expanding the use of AI across industries such as retail and customer service. Their lower computing requirements make them a more economical solution for companies, as they can operate on standard hardware without the need for expensive infrastructure. This also leads to lower operational costs and a reduced environmental impact due to less energy consumption.

For more insights into how SLMs are helping to shape customer service, visit Google’s AI research.

Faster Processing

One of the most important advantages of SLMs is their speed. With fewer parameters, SLMs can analyze and generate responses much faster than LLMs. This quick response time is crucial in fields like customer service or real-time data analysis. For instance, chatbots powered by SLMs can respond to customer inquiries instantly, improving service quality and user satisfaction. Faster data processing also leads to more dynamic and efficient marketing campaigns, as well as better internal data management within companies.

Key Limitations

However, SLMs have some limitations. Their smaller size means they have a limited information processing capacity compared to LLMs. This can result in difficulties when handling complex queries that require deeper contextual understanding or linguistic nuances. For instance, while a large language model can easily process lengthy and complex texts, an SLM may struggle with accuracy and consistency in its responses. This can limit their application in more demanding fields such as legal or medical document analysis, where precise interpretation of language is critical.

Learn more about AI-driven innovations in Meta AI Research.

SLMs Shaping the Future of AI

In a world where AI was not always accessible to everyone, SLMs are changing the game. With improvements in training techniques, hardware advancements, and efficient architecture, the gap between SLMs and LLMs is narrowing. This will make AI technology far more accessible.

With their streamlined architecture, reduced training requirements, and high efficiency, SLMs have the potential to transform many areas of business, from improving customer service with chatbots to enhancing predictive maintenance in manufacturing. SLMs offer a versatile tool that can drive innovation and efficiency across industries.