Small Language Models for Your Team’s Everyday Tasks

Introduction

Just imagine you need to take a glass of water from your kitchen. You could design a complex robot to bring you a glass of water—  wait! That would be too much. What would you do instead? You would simply use your hands–it’s easier and more efficient. Similar to how you would prefer using hands over a complex robot, a Small Language Model (SLM) is a practical choice instead of a Large Language Model (LLM) for straightforward tasks. In this article, we’ll explore how SLMs can benefit a team within an organization. We will also see how various tasks of a team can be accomplished using small language models.

Overview

• Understand what small language models (SLMs) are.
• Compare small language models with large language models.
• Explore the benefits of using SLMs in an organization.
• Discover how everyday tasks of a team can be accomplished using small language models.

What are Small Language Models (SLMs)?

SLMs are a subset of LLMs. The term “small” in small language models refers to the reduced number of parameters compared to LLMs. They have a compact architecture that requires less computational power while training and inferencing. This accelerates their training process, making them a perfect choice for domain-specific tasks with limited resources. On the other hand, LLMs are trained on huge parameters and are computationally intensive.

The table below lists some examples of small language models and large language models with their respective number of parameters.

The table clearly compares SLMs and LLMs based on their number of parameters. SLMs like Gemma, Phi3 Mini, and Llama 3.2 have significantly fewer parameters (ranging from 1 billion to 3.8 billion) highlighting their compact nature. This reduces their computational power, making their deployment easy and accessible, even in edge devices like mobile phones.

Yes! You read that right! You can now access these models within the palms of your hand.

In contrast, LLMs like GPT4o, Mistral Large 2, and Llama 3.1 have a much larger number of parameters.

How do these SLMs Maintain Quality?

Wondering how SLMs maintain their quality despite their compact size? Let’s understand this by taking the example of Llama 3.2 1B and 3B models.

There are two key techniques involved in Llama 3.2 (1B and 3B) – pruning and knowledge distillation. Let’s understand what these are.

1. Pruning

Pruning means “to cut away”. This process involved trimming down less important parts of the network from the existing model ( like Llama 3.1 B is structurally pruned to create 3.2 (1B and 3B)). The ultimate goal of this technique is to create a smaller model without affecting the original performance.

2. Knowledge Distillation

The second step after pruning is knowledge distillation, a process of extracting the most essential knowledge. This technique involves using powerful models (such as Llama 3.1 with 8B and 70B parameters ) to train smaller models (like Llama 3.2 with 1B and 3B parameters). Instead of training smaller models from scratch, the output of larger models is used during the pre-training stage to guide the smaller models. This approach helps the smaller models recover any performance lost during pruning.

After initial training, the SLMs undergo post-training, which involves similar steps to those used in Llama 3.1. This step includes supervised fine-tuning, rejection sampling, and direct preference optimization.

Furthermore, Llama 3.2 (1B and 3B) can support longer context lengths (up to 128,000 tokens), meaning they can handle large chunks of text while maintaining the quality. This feature makes the model a strong choice for various tasks like summarization, rewriting, reasoning, and more.

Small Language Models vs Large Language Models

SLMs and LLMs both follow similar concepts of machine learning from training, and data generation to evaluation, but they have some differences. Let’s look down the table below to see some important differences between SLMs and LLMs.

SLMs for Your Team’s Performance

Companies spend a large share of their budgets on software and IT. For instance, according to Splunk’s IT Spending & Budgets: Trends & Forecasts 2024, software spending is projected to increase from $916 billion in 2023 to $1.04 trillion in 2024, which is a huge number. SLMs can help reduce this amount, by reducing the budget share of language models.

Within an organization, there are several teams, and if each team has an SLM dedicated to their field, you can imagine how productive and efficient an organization can be without breaking the bank. Leveraging small language models for team collaboration, performance, and task management is quite effective in optimizing tasks.

Now, let me list a few possible tasks that a team can undertake with the help of SLMs.

1. Automate Routine tasks
2. Enhance Communication and Collaboration
3. Meeting Recaps and Task Assignments
4. Personalized Learning and Development

1. Automate Routine Tasks

Everyday repetitive tasks include drafting daily reports, feedback emails, and summarizing meeting notes. These tasks are quite monotonous and require a large bandwidth of team members. What if you could get these tasks done automatically? SLMs can make this possible. They automate routine tasks such as drafting emails, daily reports, or feedback, freeing up time for team members to focus on more complex and strategic work.

Use Case:

In the healthcare industry, patient data entry is quite a tedious task. SLMs can assist in maintaining patient records such as EHRs (electronic health records) from dictated notes, forms, or clinical worksheets, reducing the workload of hospital administrative team members.

2. Enhance Communication and Collaboration

A team comprises members from diverse backgrounds and cultures. If you are unable to understand the language or accent of any team member, it would be challenging for you to coordinate with them. SLMs can provide real-time translation services, enabling seamless communication between team members and fostering a multicultural team environment.

Additionally, SLM-powered chatbots can give precise and accurate answers to field-specific questions. This leads to improved customer satisfaction, reduced resolution times, and a streamlined support process.

Use Case:

An SLM-powered chatbot for IT services can deliver efficient and effective support, particularly in IT environments with limited resources. This automates routine inquiries and tasks, allowing IT teams to concentrate on other issues.

3. Meeting Recaps and Task Assignments

Each team member has to attend several meetings in a day. Remembering the agenda and actions of all the meetings is a  challenging task. Manually noting every point would require significant time and effort, potentially leading to the loss of crucial information. SLMs can automatically summarize meeting discussions and generate Minutes of Meetings (MOMs), streamlining follow-up tasks. To accomplish this task SLMs would need the help of speech-to-text systems to first convert the spoken words to text.

Use Case:

During the morning huddle, SLMs can transcribe and summarize the meetings, generate to-do lists, and assign them to each member,  avoiding confusion between team members.

4. Personalized Learning and Development

Upskilling is a continuous improvement process essential for the growth and success of both the team and the organization. Domain-specific SLMs can analyze team members’ performance to identify potential areas for improvement and create personalized learning experiences based on their specific needs. They can also suggest relevant articles or courses, helping the team members stay ahead of industry trends.

Use Case:

For the sales team, an SLM can start by analyzing the performance of individual members. Based on these insights, It can recommend tailored training materials comprising techniques to help them improve their sales pitch and close more deals.

Conclusion

Small language models offer dynamic solutions with low computational demands. Their small size makes them easily accessible to an organization’s broader audience. These models can automate everyday tasks and upskill team members in accordance with industry requirements. Implementing small language models for teams can improve efficiency and ensure that everyone effectively contributes to common goals.

Frequently Asked Questions

Diksha Kumari

As an Instructional Designer at Analytics Vidhya, Diksha has experience creating dynamic educational content on the latest technologies and trends in data science. With a knack for crafting engaging, cutting-edge content, Diksha empowers learners to navigate and excel in the evolving tech landscape, ensuring educational excellence in this rapidly advancing field.