SLMs Vs LLMs: The Difference between Small and Large Language Models

Language models are important in the driving of the development of Natural Language Processing (NLP). In recent years, there has been significant growth in NLP. These language models enhance the way we interact with Artificial Intelligence (AI), therefore, allowing us to use AI for tasks that require language translations such as text summarization and in chatbots. However, knowing the difference between small and large language models gives a better understanding in their working process and where they can be applied.

Language models are a powerful tool for understanding and generating human language especially in AI. These models are trained on a variety of data and have applications that spread across different verticals. Although they have a common goal (language processing), they are different in quite a number of ways. Their differences have to be put into consideration when exploring which language model to use in the development of a new AI system.

In this article, we explore the differences between small and large language models, their individual benefits, and how to choose the most suitable language model for your needs.
Here we go!

Overview of SLMs

Small language models (SLMs) are AI models that can process, understand and generate natural human language. They are small and compact language models that are trained on a smaller set of data. Their design enables them to be highly efficient and scalable. They have a few parameters (typically less than 10 billion) and can work on simpler hardware. They can be adjusted to perform specific tasks and for this reason, businesses choose them for data enhancement or to meet specific needs.

Benefits of small language models

• They are highly compatible: SLMs don’t need a large computational power which makes them good for use on devices such as mobile phones that have limited resources.
• They are accessible: They don’t always rely on cloud-based infrastructure which makes them a great option for use within a company’s premises on their own servers but still maintaining privacy and data security.
• They are affordable: SLMs require a small budget to run. Therefore, individuals or companies on a tight budget can deploy SLMs without breaking the bank.
• They have faster processing times: SLMs process information faster because they have fewer parameters to process. This feature makes them ideal for real world applications in chatbots, virtual assistants and similar applications where there is a need for faster responses.
• They are customizable: Their smaller size makes them easy to fine tune. Therefore, they are flexible and can adapt to special domains and perform specific tasks. This allows organizations to tailor them to perform specific tasks and enhance task performance.

Examples of SLMs include Pythia, Llama 3.1 8B, Phi-3.5, TinyLlama and so on

Overview of LLMs

Large language models are also a type of artificial intelligence that uses machine learning to understand, process and generate human text. However, unlike SLMs, they are trained on a much larger set of data. They also use deep learning architectures to perform NLP tasks. Typically, they have larger parameters (more than 10 billion) and work on more complex patterns. They are often employed to perform tasks where high accuracy and fluency are needed.

In LLMs, the underlying technology is a transformer neural network otherwise known as a transformer. A transformer is an AI model that adapts to human text by analyzing the patterns in a large amount of text data. By doing so, it becomes capable of understanding, processing and generating human text.

Benefits of large language models

• They have a high accuracy: These models are trained on a vast amount of data which increases their data base. Therefore they have the ability to achieve high levels of accuracy in tasks that require a deep understanding of natural language such as language translation and answering questions.
• They are versatile: Their applications vary across a wide range of industries due to their comprehensive understanding of natural language.
• They have a wide knowledge: LLMs have access to a vast amount of data with which they were trained. This wide data base enables them to handle diverse tasks, and enables them to be used in various industries.
• They are highly efficient and scalable: LLMs perform advanced tasks and their function can be distinguishable to better suit the industry and task. In order words, the scalability of these models make them ideal for any size of part of the industry.
• They have advanced capabilities: Their capabilities range from simple language translation or text generation to more complex functions such as contextual understanding of language, and fluency.

Examples of large language models include ChatGPT, Bard, Llama, Bing chat and so on.

The Difference between Small and Large Language Models

Cost effectiveness

Small language models are more cost effective than large language models. This is because the infrastructure and resources needed to deploy a small language model is smaller than those required for a large language model.

Accuracy

Large language models are more accurate than small language models. LLMs have more access to data that gives them the ability to be more accurate. In simple terms, LLMs know more than SLMs.

Fine-tuning time

LLMs on a general note cannot be fine-tuned per se. LLMs are made up of hundreds of billions of parameters. However, with the amount of these parameters they don’t need fine-tuning to adjust to a task. Although fine-tuning an AI model makes it perform tasks straightforwardly. Hence fine-tuning is important in some cases.

In cases where fine-tuning is important, LLMs are more difficult to fine-tune because of the amount of parameters they have. Fine-tuning an LLM may take months while fine-tuning an SLM can be completed in weeks.

Range of capabilities

LLMs have a wider range of capabilities and that is why they are also referred to as broad-spectrum models. They are trained on massive amounts of data from books, the internet, and other sources. They perform complex tasks that require a contextual understanding of language, language fluency and deep, multistep reasoning.

On the other hand SLMs have a limited range of capabilities and are also referred to as narrow-focused models. Their capabilities are narrow and specific like basic translation, and text summarization.

Performance and output quality

LLMs provide a more satisfactory output than SLMs. This is as a result of the different context windows of both models.
A context window is the amount of data an AI model can process at once. A larger context window, like that of LLMs, allows AI models to process longer inputs and generate outputs with a greater amount of information.

Conclusion: Choosing the right language model for your needs

Each language model has its uniqueness and its applications depend on its uniqueness. Despite the fact that large language models may seem favorable in many situations, there are also instances where only small language models can be used. For example, the easy fine-tuning ability of a small language model adds an extra layer of flexibility to it which allows a wide application of this model. At the same time, industries requiring more complex tasks to be done need large language models.
Nowadays, there are also AI platforms that support the use of LLMs. One of such platforms is Openfabric AI platform. The use of LLM on the platform enhances users’ experience in the use of these AI tools. Users can use natural language to input prompts while LLM models understand the context of these prompts, process them and translate the texts. After translation, these tools then generate accurate results in response to the imputed prompts.

Ultimately, the decision on which language model to choose entirely depends on what needs to be done. A good understanding of the benefits of each model and the differences between small and large language models gives a better understanding on how to choose an AI model.

To learn more about AI and AI models, visit our WEBSITE today.