Small Language Models (SLMs): The Compact Powerhouses of AI

Large Language Models (LLMs) have been widespread for a couple of years and it is still evolving to be more cost effective as well as energy-efficient. While large language models (LLMs) like OpenAI's GPT-4 or Google's Bard often steal the limelight, Small Language Models (SLMs) play an equally important role in achieving cost-effective AI solutions. Now, you can run LLM locally as well, which we covered in a different blog, then what are SLMs and how do they work

In this blog, we’ll explore the following topics to help you understand the basics of Small Language Models and how your business would be impacted by its integration:

• What are Small Language Models and their importance?
• How do Small Language Models work?
• The difference between SLM undefined LLM (SLM vs LLM)
• Benefits of Small Language Models
• Business use cases of SLMs
• How can SLM improve business efficiency?

What Are Small Language Models (SLMs)?

Small Language Models (SLMs) are NLP language models with compact learning parameters, designed specifically to perform quick tasks that do not require much computational power. Various text-based tasks such as sentiment analysis, translation, and chatbot interactions, with a significantly smaller computational footprint, can be ideal examples. LLMs have billions of training parameters, whereas SLMs are optimized for speed, efficiency, and simplicity, often containing millions or even fewer parameters. To name a few of the SLMs that the organizations already use are:

• Llama 3.1 8B
• Gemma2
• Qwen 2
• Mistral Nemo
• Phi-3.5, and more.

Why Small Matters?

SLMs are ideal for resource-constrained environments like mobile devices, embedded systems, or small-scale businesses. Considering you want to transfer a coffee table to a new place. Now, you can hire a commercial heavy truck to carry a single piece of furniture, which would eventually get the work done. However, a pickup truck would be an ideal choice, as it would be quick, cost-efficient, accessible, and use less fuel. Similarly, SLMs excel at providing practical solutions without the overhead of complex infrastructure.

How Do Small Language Models Work?

Small Language Models are language models with fewer learning parameters, so they rely on the same foundational principles as LLMs. Let's go through them to understand:

Training on Text Data: Patterns, relationships, and structures in text are analyzed through datasets that may be smaller than those used in LLMs.

Transformer Architecture: SLMs have a reduced parameter size with fewer layers of transformer architecture.

Fine-Tuning: SLMs are most often fine-tuned to perform specific tasks to make them faster and more accurate in limited domains.

Prioritizing efficiency over-generalization has helped SLMs to deliver focused performance with reduced training and inference times.

Small Language Model vs Large Language Model

If we consider a general-purpose model, then the Large Language Model (LLM) would make a lot of sense given its high computing performance. However, for a specific task-based computation where speed is important, a Small Language Model (SLM) is the best choice.

Let us now dig into how SLM is different from LLM in terms of performance, cost, and more aspects. Here’s a quick comparison to highlight their differences:

SLMs are the pragmatic choice for businesses with limited resources or specific use cases, whereas LLMs are better suited for complex and diverse requirements.

Benefits of Small Language Models

• Cost Efficiency: AS SLMs require reduced computational requirements, the operational cost is also reduced.

• Speed: SLMs deliver quicker responses, due to their smaller training parameters, which is essential for real-time applications.

• Accessibility: SLMs are quite accessible and can run on edge devices like smartphones and even low computational IoT systems.

• Sustainability: As SLMs can run on lower computational power, they use less energy, making them eco-friendlier than LLMs.

• Customizability: Because of the smaller training parameters, SLMs are easier to fine-tune for narrow domains or specific tasks.

Business Use Cases of SLMs

SLMs for businesses shine in scenarios where precision, speed, and efficiency matter. A few Business applications of SLM are as discussed below:

1. Customer Support Chatbots:

FAQs, appointment bookings, or troubleshooting can be easily managed by an SLM for faster delivery and accurate responses.

2. Sentiment Analysis:

Businesses can monitor customer feedback in real-time if it's negative or positive and reply or take actions based on the result to enhance the customer experience.

3. Text Summarization:

Internal reports, news articles, or product reviews can be summarized with SLM, helping businesses take quick action.

4. Voice Assistants:

Smart speakers or AI car assistants can use SLMs to understand undefined process commands locally and reciprocate with a proper response.

5. Document Classification:

A cluttered document can be dangerous and cost one's business, and there an SLM can be handy to efficiently sort emails, legal documents, or financial reports into predefined categories for better access.

How SLM Can Improve Business Efficiency?

SLM has the ability to efficiently enhance business operations by automating repetitive tasks that yield better customer interaction at a significantly reduced cost.

For example, an SLM powered real-time chatbot ensures quick and efficient responses, which enhances customer satisfaction. SLMs do not require expensive infrastructure for their functioning, making them ideal for small and medium enterprises (SMEs). SLMs help streamline the workflow of any operation to free up valuable human resources. Their scalability and accessibility allow businesses to adopt AI solutions seamlessly, ensuring growth without technical overwhelm or high operational costs.

Incorporating SLMs can significantly impact businesses in the following ways:

Enhanced Productivity: Automating repetitive tasks like email sorting or report generation frees up valuable human resources.

Improved Customer Experience: Real-time chatbots powered by SLMs ensure quick and satisfactory customer interactions.

Lower Overheads: Reducing dependency on cloud-based LLMs cuts down infrastructure costs.

Scalability: SLMs are easier to integrate into existing workflows without requiring extensive upgrades.

For small and medium enterprises (SMEs), SLMs can act as a stepping stone to adopting AI without overwhelming technical complexity.

End Note

SLM might not be the highlighter as their larger counterparts, but they are quite handy for SMEs who require a snappy AI solution that is affordable, scalable, and efficient for their business operations. With their ability to deliver focused, high-performance results at a fraction of the cost and complexity of LLMs, SLMs would truly shine in automating business operations. SLMs provide a practical solution for any business looking for a customized and responsive AI solution in their garage.

We, at Seaflux, are AI undefined Machine Learning enthusiasts, who are helping enterprises worldwide. Have a query or want to discuss AI projects where SLMs undefined LLMs can be leveraged to improve business operations? Schedule a meeting with us here, we'll be happy to talk to you.