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Deploying large language models (LLMs) presents several challenges, including high computational and memory requirements, which demand expensive hardware and cloud infrastructure. Their energy consumption is significant, leading to lofty operational costs and environmental impact.
LLMs can also introduce latency, slowing real-time applications. They're also difficult to scale efficiently.
Financially, both training and running LLMs are costly, making them inaccessible for smaller organizations. They also carry ethical risks, such as generating biased content or exposing sensitive data.
Overall, LLMs are resource-intensive and complex, limiting their practicality for many real-world applications. Enter small language models.
A New Model Emerges
Organizations looking to generative AI for a competitive advantage may find interest in small language models as a way to cut back on the cost and difficulty of developing and using their own LLM.
Small language models (SLMs) offer a practical solution for harnessing the power of AI without the heavy computational demands of large models. By reducing model size and optimizing efficiency, SLMs enable faster processing, lower costs and easier deployment across a wide range of devices, from smartphones to IoT systems.
Despite their compactness, these models can handle various language tasks effectively, making them ideal for businesses and applications where resources are limited.
In an April 2024 post, Sally Beatty of Microsoft explains how SLMs offer several key advantages over LLMs in certain scenarios, making them a preferable choice for many businesses and applications.
"Small language models are well suited for organizations looking to build applications that can run locally on a device (as opposed to the cloud) and where a task doesn’t require extensive reasoning or a quick response is needed," she writes, noting that they also offer potential solutions for regulated industries and sectors that encounter situations where they need high quality results but want to keep data on their own premises.
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Choosing LLM vs. SLM
It is clear that SLMs and LLMs come with their respective set of pros and cons, and organizations have to take several factors into consideration to determine which is right for them.
A Splunk article explains the similarities and differences.
On the similarity front, the author writes, both SLMs and LLMs operate on the same principles; both are based on similar probabilistic machine learning principles for their architecture, training, data generation and evaluation.
The most obvious difference, the author notes, is model size. LLMs, like ChatGPT (GPT-4), reportedly have 1.76 trillion parameters, while SLMs, like Mistral 7B, contain around seven billion. This size disparity results from differing architectures: ChatGPT uses a self-attention mechanism with an encoder-decoder setup, while Mistral 7B employs sliding window attention in a decoder-only model for more efficient training.
Additionally, the author explains, SLMs are typically trained on domain-specific data, excelling in those areas but lacking broader context across multiple domains. LLMs, in contrast, aim for a broader emulation of human intelligence, trained on larger datasets to perform well across a variety of tasks. This makes LLMs more versatile and suitable for adaptation to a wider range of downstream tasks, such as programming.
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The SLM Advantage
While LLMs certainly have their role and place, the advantages to SLMs for smaller organizations are clear. They include:
- Faster deployment: SLMs can be deployed quickly, reducing waiting periods and enabling rapid integration into existing systems.
- Lower computational requirements: SLMs require less processing power and memory, making them faster to train and deploy.
- Real-time processing: SLM speed makes them ideal for applications requiring real-time language processing, such as interactive chatbots or live translation services.
As an engineer who builds software for organizations of all sizes, Seth Black says there are two other lesser-known workplace advantages to SLMs:
- They ingest information significantly faster than LLMs. LLMs can take months to retrain, while SLMs can take minutes or hours.
- They need less specific prompting because they already have the context they need. In contrast, LLMs need to be told "you are reviewing legal documents for a multi-unit commercial real estate contract in California, act like a commercial real estate attorney in California."
Black says a practical use case example of LLMs would be feeding it a company handbook, with job roles and descriptions, company history and relevant documentation, and then using that SLM to help build out a custom employee onboarding experience.
“Another example," he said, "would be feeding another SLM security/operational control documentation and job descriptions; then using that SLM to craft training paths for each employee that keeps the CISO happy."
SLMs in the Digital Workplace
SLMs emerged to meet very specific needs — and a gap in that market. According to Steve Fleurant, CEO of IT services provider Clair Services, SLMs are designed with efficiency in mind and fit easily into digital workplaces. They are essentially streamlined versions of larger language models, achieving impressive performance with significantly fewer parameters.
This reduction in size directly translates to several crucial advantages:
- Accessibility: SLMs are more accessible. They require less powerful hardware, making them viable for individuals and smaller businesses who might not have access to the extensive resources needed for larger models.
- Speed: SLMs are faster. Processing and generating text takes less time, which is crucial for applications requiring real-time responses.
- Cost: SLMs are more cost-effective to deploy and operate, both in terms of initial investment and ongoing energy consumption. Their lower computational demands, Fleurant said, mean lower cloud computing bills and a smaller carbon footprint.
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“Their compact size enables them to be integrated into a wider range of applications, from smart assistants and chatbots to translation tools and content generation software,” Fleurant said. "They can be used to automate repetitive tasks, personalize user experiences and provide quick access to information.”
Imagine, for instance, a small business using an SLM to power a customer service chatbot that can handle common inquiries efficiently, freeing up human agents to address more complex issues.
Fleurant notes that another advantage of SLMs is that they can be deployed "at the edge," meaning, for example, running the model directly on a device like a smartphone, IoT sensor or web browser, rather than relying on a connection to a powerful server in the cloud. Deploying at the edge dramatically reduces latency — the delay between sending a request and receiving a response.
“This is crucial for applications requiring instantaneous feedback, such as real-time translation during a conversation or controlling a robot in a dynamic environment,” he said.
Clarke Duncan, the founder of OutsourcingStaff, sums up the SLM advantage in the digital workplace in three points:
- Task-Specific Optimization: SLMs can be fine-tuned for specific tasks such as customer support, content generation or language translation. This specialization ensures that they perform efficiently and effectively, providing reliable outputs for particular applications.
- Integration With Existing Systems: SLMs' lightweight nature allows for easier integration with existing digital systems and platforms, facilitating seamless workflow automation and improving overall productivity. This allows businesses to handle operations in real time, delivering tailored communication experiences. In a rapidly changing digital landscape, offering this level of personalization is key to maintaining a competitive edge, Duncan said.
- Data Privacy and Security: Smaller models can be deployed on-premises, which enhances data privacy and security by keeping sensitive information within the organization’s infrastructure.
By enabling efficient and cost-effective AI solutions, small language models are poised to play a significant role in the future of digital work, particularly for small to medium-sized businesses looking to optimize their operations.
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SLM Use Cases
We've already covered several examples of how SLMs can add to the competitiveness of small to mid-sized organizations.
- Support: One key application is in the realm of customer support. Language models can help answer FAQs and troubleshoot common problems faster and with high accuracy, improving customer experience while reducing operational costs, said Riccardo Ocleppo, the CEO of the Open Institute of Technology.
- Office productivity tools: They can assist in automated proofreading, email drafting and summary generation, thus increasing overall efficiency.
- Data management: Small models can categorize, tag and index data, reducing the burden on human personnel.
- Security monitoring: SLMs can handle routine inquiries about security incidents, immediately responds to common questions and guiding users in performing basic troubleshooting steps, added Baran Erdoğan, which develops Offensive Security Manager. In this light, this capability enables human agents to handle more complex issues faster, thus improving response times while enhancing user experience at a fraction of the operational costs.
Beyond security monitoring, Erdoğan says SLMs are also being used for managing content for cybersecurity teams, helping prepare reports, compliance documentation and training content and thereby freeing up more strategic time for cyber teams to spend on tasks like vulnerability assessment and threat modeling. At his company, the models are used to provide full-scale vulnerability reporting, synthesize results across a number of security assessments and allow analysts to focus on implementing remediation strategies.
Then, there's the power of SLMs within HR and recruiting.
Ultimately, for the digital workplace, small language models offer easy integration into workflows to automate routine tasks, enhance real-time communication and improve productivity. Their low computing requirements make them ideal for businesses seeking efficient solutions without heavy resource demands, which is essential in a dynamic digital environment.
“The use of small language models is revolutionizing the digital workplace across multiple disciplines,” Ocleppo said. "Their range of functions, efficiency and cost-effectiveness make them a promising avenue for shaping future digital workplaces."