Why Smaller and Specialized Language Models will Lead the Way in the Future of Generative AI
We have progressed from the dawn of AI to today’s large language models (LLMs), with giants like GPT-4 making significant waves. Now, there is a shift happening. Businesses are moving away from the one-size-fits-all Large Language Model (LLM) approach and recognizing the transformative potential of smaller, specialized language models that are finely tuned to their unique requirements.
Let us first understand the basic differences that set the stage for appreciating how the choice between Large Language Models (LLMs) and Small Language Models (SLMs) can significantly impact AI applications based on specific business needs and objectives.
Let us now move forward to understanding why SLMs will take the lead in the future of Generative AI (Gen AI).
Related article for you: What’s Inside the World of Generative AI?
I. SLMs Bring Cost Savings and Feasibility
LLMs, with their hundreds of billions or even a trillion parameters, strain computing resources and incur high costs. Recognizing the impracticality of scaling these models even further due to computational limitations, experts suggest a strategic shift. To save costs and make AI more practical, businesses are strategically transitioning to smaller, more focused models, tailored for specific industries. This transition is not just a recognition of businesses’ unique needs but a significant shift in terms of monetary impact.
Reducing the number of parameters in these models (making them smaller and more focused), requires less computing, making these models more feasible to develop and use. This eventually translates to improvement in cost-effectiveness. It is a strategic move aimed at achieving more with fewer resources.
Starting at a modest 7 billion parameters versus the industry standard 70 billion, these models usher in a new era of budget-conscious AI.
II. SLMs offer Specialization, Precision, and Relevance for Business Excellence
From a business standpoint, the shift towards SLMs is driven by the need for solutions that are finely tuned to meet specific industry requirements.
➔ Firstly, SLMs are more specialized as they are designed to cater to the unique demands of specific business domains. Unlike LLMs, which are more general-purpose and cover a wide range of topics, SLMs can be customized to focus on the specific language and intricacies of a particular industry or application. This specialization ensures that the model understands and processes domain-specific information with greater accuracy.
➔ Secondly, precision is a key factor that distinguishes SLMs in the business landscape. Large models often come with a wealth of general knowledge that may not be relevant to specific use cases. SLMs, on the other hand, eliminate the noise by concentrating on the essential information required for a particular business application. This precision enhances the model’s ability to provide accurate insights and solutions tailored to the specific needs of the business.
➔ Thirdly, relevance is crucial for business success, and SLMs excel in delivering content that is directly pertinent to the industry or application at hand. The customization of SLMs allows businesses to filter out irrelevant information and focus on what matters most to their operations. This heightened relevance ensures that the AI models generate insights that directly contribute to informed decision-making within the business context.
III. SLMs offer a Strategic Edge through Custom Training and Data Ownership
There are strategic advantages that companies gain when they choose to train AI models directly on their data and infrastructure. Instead of relying on pre-trained models or models hosted externally, companies undertake the task of training AI models using their own specific datasets and within their own computing environments.
This approach provides a more tailored and context-specific learning experience for the AI, as it becomes intimately familiar with the intricacies of the company’s unique data.
By owning and custom training their AI models, companies gain a competitive edge and stand out in the market. This differentiation comes from having greater control over the AI’s learning process, enabling customization to match specific business needs, and ensuring the protection of proprietary data and intellectual property.
IV. SLMs Improve Data Privacy and Control through Containment Strategies
Given the significance of data privacy and security, having a well-defined containment strategy is crucial for clients leveraging Intelligent Automation or GENAI solutions. SLMs hold a significant edge over their LLM counterparts due to the critical aspect of containment. The emphasis on containment in SLMs translates to enhanced data security and privacy for businesses.
SLMs are often trained within a client’s own infrastructure, minimizing exposure to external networks, and safeguarding proprietary information. This containment strategy not only aligns with stringent data protection requirements but also empowers businesses with a higher degree of customization and control over the AI training process.
Moreover, containment in SLMs addresses concerns related to unintended knowledge transfer, a risk associated with LLMs. The focused training of SLMs within specific domains ensures that the model generates outputs relevant to the intended context, mitigating the possibility of undesirable or inappropriate results.
V. SLMs Overcome Challenges with Proprietary LLM APIs
A significant advantage of SLMs lies in overcoming the hurdles posed by external LLM APIs, including issues related to latency, dependency, and data privacy. SLMs, often trained within a client’s own infrastructure, mitigate concerns about latency by minimizing reliance on external APIs, ensuring quicker response times for real-time applications.
There are challenges faced by models like OPENAI’s ChatGPT, where availability and uncontrolled workloads pose significant issues. Even under subscription models, the GPT-4 model imposes usage limits, directing users towards the metered API path. This limitation, coupled with the necessity for an API monitor to track availability and potential bottlenecks, highlights the practical difficulties associated with relying on external LLM APIs.
In response, businesses are increasingly leaning towards the adoption of SLMs, where local training and containment practices mitigate these challenges, providing a more reliable, secure, and customizable alternative for their specific needs.
Summary
In the future of GenAI, the rise of SLMs is clear. Overcoming cost and resource challenges, SLMs offer tailored, precise solutions. Their strategic advantages in differentiation and ownership, fortified by robust containment, define a new era. SLMs lead the way, reshaping AI for cost-effectiveness and unparalleled customization in diverse business landscapes and unlocking unprecedented possibilities in the process.
As Artificial Intelligence continues to evolve, the incorporation of Gen AI into ServiceNow is unfolding new possibilities for businesses. If you are navigating the vast landscape of ServiceNow and wanting to maximize the potential of its AI features, ServiceNow experts at ADVANCE Solutions are here to support you. We are a certified Gen AI partner of ServiceNow and a trusted Elite ServiceNow partner.
Connect with our ServiceNow experts to improve your ServiceNow experience.
