Does the Size Matter? Large vs. Small Language Models
Generative Artificial Intelligence (GenAI) has made incredible progress, impacting many parts of our daily lives. At the center of this revolution are language models, which allow machines to understand and generate human language. These models vary in size, from the massive GPT-4 to smaller, task-specific ones. As AI evolves, a key question arises: does the size of a language model really matter? In this blog post, I’ll explore this by comparing large and small language models, discussing their strengths, limitations, and practical uses. Read on to find the best solution for your needs.
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Disclaimer: to make things a bit easier, when I’m referring to AI I mean Generative AI.
The quick development of large language models (LLMs) like OpenAI’s GPT-4 and Google’s Gemini has captured the imagination of the public and professionals alike. Boasting trillions of parameters, these models can perform an impressive array of tasks, from generating creative content to engaging in complex conversations. Their ability to understand and generate nuanced human language has set a new benchmark in AI capabilities. However, the sheer size and computational requirements of these models also pose significant challenges, including high costs and substantial energy consumption influencing their carbon footprint. This raises the question of whether such massive models are always the best choice for every application.
Small language models (SLMs) offer an interesting alternative. These models, while lacking the extensive parameter sets of their larger counterparts, are designed to be highly efficient and task-specific. They require fewer computational resources and can be trained and deployed more quickly and cost-effectively.
Despite their smaller size, SLMs can achieve remarkable performance in specific domains, making them a viable option for businesses and applications with limited budgets or specialized needs.
This efficiency and adaptability make SLMs an attractive proposition, particularly in environments where speed and resource optimization are essential.
What are Language Models?
In the early days of language models, they were just trivial algorithms that put the most common word after the previous word. You may remember similar mechanisms in your smartphone when it proposes your next word while typing a message. The simplicity of this procedure was very inadequate and was not ready to be implemented in numerous functionalities.
Nowadays, language models are significantly more complex. Although they are still based on statistics, they leverage neural networks and advanced computation, leading to the repetitive prediction of the next token, the smallest portion of the language that language models operate on and can generate - it may resemble syllables at first. Such generation depends on many parameters. Not only do they use statistics, but they also take user inputs and perform thorough data analysis to deliver the best value for the user.
How Do They Work?
Language models operate on the principles of machine learning. They ingest large volumes of text data, learning the statistical relationships between words and phrases. During training, the model adjusts its parameters, which are numerical values transforming the neuron output (neuron output is the result of what the neuron “thinks” after processing the information it was given), to minimize prediction errors, gradually refining its ability to generate text that mirrors human language. Advanced models, such as those based on transformer architectures (transformer architectures are a type of advanced model in machine learning designed to better understand and generate human language), utilize mechanisms like attention to better understand context, making them incredibly proficient at handling complex language tasks.
This architecture optimizes the language models by providing new ways to analyze text by focusing on relevant input parts. Some language models implement a technique called Chain of Thought (CoT) prompting. This technique helps the model break down complex problems into smaller, manageable steps. This improves its ability to handle tasks that need multiple stages of thinking, using the attention mechanism to focus on important parts along the way.
What are LLMs? Examples of LLMs
Large Language Models (LLMs) are the most popular type of language models due to their general-purpose use and widespread availability. These models, like OpenAI’s GPT-4 and Google’s Gemini, are distinguished by their vast number of parameters, which, at the time of publishing this blog post, can be counted in trillions, allowing them to utilize the full spectrum of language features. GPT-4, for instance, can perform tasks ranging from simple text completion to sophisticated content creation, thanks to its billions or even trillions of parameters. They are also capable of classifying input, which can be advantageous for optimizing operations in enterprises, such as the automatic classification of customer service tickets.
While GPTs are a powerhouse, they’re not the sole option in the field of LLMs. Each model of the LLM can have its specialty in which it excels. For instance, Anthropic’s Claude Sonnet 3.5 seems to be better at coding than GPT. Our experience also shows that there is a difference between the vision models of LLMs.
We would rather recommend GPT-4o for tasks demanding attractive descriptions, but Vertex AI is better for thorough, detailed descriptions of the given image or document.
This is because each model was trained on various datasets and its training could have a different approach, which caused it to perform specific tasks better than other models.
It is important to have experience with different models as there might be more use cases like the previously mentioned one, and such familiarity may save a lot of effort when trying to do something with an irrelevant AI model. Other great examples are local language models, which are trained in languages other than the global ones to serve the best results for native speakers of those languages. The best examples of such local LLMs are OpenThaiGPT for the Thai language and Bielik LLM for Polish. When you converse with such AIs, your experience is unbelievably different, and such chats can bring much more authentic impressions, which can be useful when creating products based on communication with customers.
What are SLMs?
Small Language Models (SLMs) are more compact versions of their larger counterparts. While they lack the extensive parameter sets of LLMs, SLMs are designed to be efficient and adaptable for specific tasks. They are designed to outperform any other Language Model in a specific range of tasks. Therefore, there is a possibility to limit the training dataset, time, and needed resources, which cuts down the training costs in an enormous way. This makes them accessible and practical for businesses and applications with limited budgets or processing power. This does not mean they are worse; they are just much more specific. Keeping that in mind, we can surpass the LLMs regarding costs if we specify the tasks extremely precisely and focus more on coherent datasets, architecture, and methodology.
The examples of actions where the training of SLM could be beneficial include hardware issue type recognition based on the user’s input or helping the user provide feedback while filling in complex forms, including financial wordings and specific domain knowledge. In the vast majority of cases where the LLM was applied, the SLM would work and, in my opinion, would perform equally or even better, and I would no longer invest money in custom LLM training as there are plenty of ready-to-go solutions. Of course, there would be some cases where the LLM is the best choice, like when working with nomenclature of various fields.
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LLMs vs. SLMs - Their Examples e.g., ChatGPT
The distinction between LLMs and SLMs primarily lies in their scale, purpose, and application scope. LLMs, with their expansive parameter sets, excel in tasks requiring extensive knowledge and nuanced language understanding, but they may not be as successful in specific tasks as SLMs. For example, GPTs used in ChatGPT are LLMs that can engage in sophisticated dialogues, generate creative content, and more. In contrast, SLMs retain much of their capability while being faster and more resource-efficient. These models are ideal for applications where speed and efficiency are prioritized over deep language comprehension. They are also very cheap to train. In my opinion, if you have a repetitive task conducted by human review, especially when it concerns a specific or unusual domain, e.g., technical, financial, or medical, you can easily train an SLM that will do the job much faster and cheaper than an LLM by at least a few orders of magnitude.
When to Use SLMs? Things That SLMs Can Do in Contrast to LLMs
SLMs shine in scenarios where computational resources are limited, or the task at hand doesn’t require the vast universality of an LLM. Examples include real-time applications like customer support chatbots, where response speed and relevance of the answer are crucial, or embedded systems in mobile devices that necessitate efficient use of power and storage. SLMs can perform remarkably well in these contexts, offering quick and relevant responses without the overhead of larger models, and I find them the best choice to consider. Even if you need more context for the language model to handle, according to lean methodologies, you can start with one SLM, and when scaling the functionalities and AI responsibilities, you can also add more specific SLMs implementing either Mixture of Experts or Swarm architectures.
Why SLMs Are So Cheap to Train and Why It Is Their Biggest Advantage
The development of LLMs may result in bills ranging from tens of thousands to millions of dollars, especially if we aim to create a competitor for GPT-4 or similar models. In contrast, SLM training costs start from just a few dollars. The training efficiency of SLMs stems from their reduced complexity and parameter count. This means they require less computational power, memory, and dataset resources, significantly cutting down training time and costs. For businesses, this translates to faster deployment and lower operational expenses, making AI-driven solutions more accessible and economically viable.
Training an LLM to perform specific tasks could be like using a Ferrari for pizza delivery - a scooter may just outperform it.
Conclusion
In conclusion, while ChatGPT has highlighted the power of LLMs, the wider range of language models, including SLMs, offers fantastic opportunities for various applications at a much lower cost. Nearly every business need addressed by LLMs can be met by cleverly implementing SLMs. By understanding these models and their best use cases, we can truly tap into AI’s full potential, driving innovation and efficiency across different fields.
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