Language models are systems that can generate natural language texts based on specific inputs, such as words, phrases, or topics. They are widely used in various applications, including speech recognition, machine translation, text summarization, and conversational agents. However, not all language models are the same. Some models are exceedingly large, containing hundreds of billions of parameters and requiring massive amounts of data and computing power to train and run. These models aim to achieve general-purpose natural language understanding and generation across a wide range of domains and tasks. However, these models also pose significant challenges, such as high computational costs, environmental impacts, ethical issues, and data availability. Therefore, there is a growing interest in exploring smaller language models, which can offer comparable or even superior performance on specific tasks and domains—such as legal documents, medical records, product reviews, etc.—while requiring fewer resources and less data.

Below, AHEAD experts answer some of the fundamental questions surrounding small language models to provide a better understanding of the current state and future uses of smaller language models in AI:

1. What are the advantages of smaller models?

The advantages of smaller models lie in their lower resource requirements, ability to handle specialized tasks, and cost-effectiveness. While larger language models like GPT-3 are trained on vast amounts of data from various sources, smaller models are purpose-built for specific tasks. This focused training allows them to excel in their intended applications without the need for excessive computational resources or spending. It’s important to note that having one powerful language model that can generalize across many tasks may not always be the most efficient approach. Smaller models designed for specific tasks, such as sentiment analysis, note summarization, and question-answering based on contextual information, offer targeted and efficient solutions for businesses and users alike.

2. What other companies are using smaller models?

Many companies are now turning to smaller models to leverage generative AI in a more task-focused manner. These include companies that operate on edge devices, IoT systems, and other resource-constrained machines. By using smaller models, these companies can still benefit from generative AI capabilities while ensuring optimal performance on their limited hardware.

3. How will smaller models be useful for commerce?

Smaller models have the balance between performance and efficiency that makes them well-suited for various commerce applications that require quick response times and specialized tasks. Whether it’s providing personalized shopping recommendations or handling customer queries, smaller models can deliver accurate results promptly. Additionally, smaller language models can be deployed on low-powered devices such as in-store help kiosks, allowing businesses to provide AI-powered assistance directly to customers. This enhances the overall customer experience by reducing processing times for administrative tasks, such as returns or inquiries about product availability. By optimizing performance and cost-effectiveness, smaller models offer practical solutions in the world of commerce.

With smaller models offering a feasible alternative for generative AI applications that require high efficiency and specificity, they allow companies to deploy generative AI on various platforms and devices, regardless of their resource constraints. These models are a great way to lower the barrier to entry for AI solutions, which in turn means that more organizations can use them to enhance customer satisfaction via faster and more customized services.

To learn more about the ever-growing applications of artificial intelligence for enterprise, reach out to our team today.