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We’ve all asked ChatGPT to write a poem about lemurs or requested that Bard tell a joke about juggling. But these tools are being increasingly adopted in the workplace, where they can automate repetitive tasks and suggest solutions to thorny problems.
These large language models (LLMs) have garnered attention for their ability to generate text, answer questions, and perform various tasks. However, as enterprises embrace AI, they are finding that LLMs come with limitations that make small language models the preferable choice.
Why are Enterprises Using LLMs?
LLMs are massive machine learning models that have been trained on millions of data points to be able to understand and generate human languages. What makes modern LLMs like Bard and GPT-4 particularly useful is the speed at which they can generate easily readable text. Enterprises are adopting these models and LLM-powered apps to draft memos and presentations, edit emails, answer questions, summarize copy and more. By leveraging LLMs, many enterprises are increasing efficiency and decreasing costs, but as usage grows, they’re uncovering the limitations of LLMs.
Limitations of LLMs
One key limitation of LLMs is their size and computational requirements. Because Large Language Models are trained on millions of data points, training and maintaining an LLM is resource-intensive and requires significant computing power for training and deployment. This can lead to high costs and inefficiencies for enterprises. Additionally, LLMs have been known to introduce biases from their training data into their generated text, and they may produce information that is not factually accurate.
Small Language Models are the Way of the Future
Enterprises are now turning away from full-size LLMs to smaller language models, sometimes referred to as edge language models, that are customized to their specific needs. Here's why:
Efficiency
Small language models are more efficient to train and deploy. They require less data to train and can run on less powerful hardware, resulting in cost savings for enterprises that are looking to optimize their computing expenses.
Accuracy
Due to their smaller scale, edge AI models are less likely to exhibit biases or generate factually inaccurate information. With targeted training on specific datasets, they can more reliably deliver accurate results.
Customization
Small LLM models offer greater customization potential. By training them on proprietary or industry-specific datasets, enterprises can tailor the models to their specific needs and extract maximum value from their AI investments. This flexibility allows for better alignment with business objectives.
Security
Small language models offer advantages in terms of safety and security compared to large language models. Here's how smaller models enhance safety and security in AI applications:
Smaller Codebases
Smaller models have a smaller codebase and fewer parameters compared to LLMs. This reduced complexity minimizes the potential attack surface for malicious actors. By fine-tuning the large language model based on size and scope, the potential vulnerabilities and points of entry for security breaches are significantly reduced, making small language models inherently more secure.
Control over Data
Another aspect is the enhanced control over training data. Edge ML models can be trained on specific, curated datasets, which provides greater control over the quality and integrity of the training data. Practicing machine learning on small datasets allows enterprises to mitigate the risks associated with biased or malicious data that could lead to biased outputs or unethical behavior. By carefully selecting and validating the training data, small language models can ensure safer and more secure AI applications.
Simpler Risk Assessment
Moreover, fine-tuned language modeling can be specifically designed to prioritize safety and security considerations relevant to an enterprise's needs. By focusing on specific use cases and datasets, micro models can undergo rigorous AI risk assessment and validation processes tailored to the organization's requirements. This customized approach enables enterprises to address potential security vulnerabilities and threats more effectively.
Transparency
Additionally, small language models tend to exhibit more transparent and explainable behavior compared to complex LLMs. This transparency enables better understanding and auditing of the model's decision-making processes, making it easier to identify and rectify any potential security issues. By having insights into how the model operates, enterprises can ensure compliance with security protocols and regulatory requirements.
Boundaries
Furthermore, edge machine learning models often process data locally or within a controlled environment for offline AI capabilities, reducing the need for data to leave the organization's premises. This approach helps protect sensitive information and maintains privacy, reducing the risk of data breaches or unauthorized access during data transmission.
While small language models provide these safety and security benefits, it is important to note that no AI system is entirely immune to risks. Robust security practices, ongoing monitoring, and continuous updates remain essential for maintaining the safety and security of any AI application, regardless of model size.
Discover the best LLM for coding based on your workflow in our latest post.
Applications of Small Language Models
Examples of small language model applications in enterprise settings include:
Enhancing productivity: Small language models can be trained on enterprise-specific data, so the answers the models generate are tailored to your team. This can help onboard new team members, ensure that distributed teams are still working with similar information, and even allow employees to get company-specific answers to questions without interrupting a coworker.
Pieces for Developers drives productivity with some of the most advanced edge ML models on the market. The application allows developers to save, share, enrich, and reuse their code snippets, and their edge machine learning models are small enough to live on your computer and function without an internet connection.
Customer service automation: Micro-models can automate customer service tasks by answering questions and resolving common issues. This frees up human representatives to focus on more complex and personalized customer interactions.
Ada is one AI startup tackling customer experience— Ada allows customer service teams of any size to build no-code chat bots that can interact with customers on nearly any platform and in nearly any language. Meeting customers where they are, whenever they like is a huge advantage of AI-enabled customer experience that all companies, large and small, should leverage.
Sales and marketing optimization: Small language models can generate personalized marketing content such as tailored email campaigns and product recommendations. This empowers businesses to enhance their sales and marketing efforts and drive better results.
ViSenze develops e-commerce product discovery models that allow online retailers to suggest increasingly relevant products to their customers. They deliver strong ROI and a better experience for shoppers, making them an all-around win.
Product development support: Edge models can aid in generating new product ideas, testing features, and predicting customer demand. By leveraging these models, businesses can develop more refined products and services that cater to customer preferences.
One way to implement emerging AI technologies to augment your team is by trying Applitools to test your software. Applitools claims to reduce the time it takes to establish automated tests by leveraging AI to reduce manual work.
Conclusion
When compared to LLMs, the advantages of smaller language models have made them increasingly popular among enterprises. Their efficiency, accuracy, customizability, and security make them an ideal choice for businesses aiming to optimize costs, improve accuracy, and maximize the return on their future AI tools and other investments.
