An SLM focuses on specific AI tasks that are less resource-intensive, making them more accessible and cost-effective.¹ SLMs can respond to the same queries as LLMs, sometimes with deeper expertise for domain-specific tasks and at a much lower latency, but they can be less accurate with broad queries.² LLMs are an excellent choice for building your own enterprise custom agent or generative AI applications because of how capable they are.

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Compare SLMs versus LLMs

Here are some criteria for each model type shown side-by-side to help you evaluate at a glance before diving deep into your due diligence and choosing one approach over another.

SLM and LLM functions

When comparing functions for small versus large language models, you should consider the balance between cost and performance. Smaller models typically require less computational power, reducing costs, but might not be well-suited for more complex tasks. Larger models offer superior accuracy and versatility but come with higher infrastructure and operational expenses. Evaluate your specific needs, like real-time processing, task complexity, and budget constraints, to make an informed choice.

You should also consider that SLMs can be fine-tuned to perform well in required tasks. Fine-tuning is a powerful tool to tailor advanced SLMs to your specific needs, using your own proprietary data. By fine-tuning an SLM, you can achieve a high level of accuracy for the particular use cases you require without needing to deploy an LLM that could be more expensive.  

For more complex tasks with a lot of edge cases, such as natural language queries or teaching a model to speak in a specific voice or tone, fine-tuning LLMs is a better solution. 

SLM and LLM features

Also be sure to consider features like computational efficiency, scalability, and accuracy. Smaller models often offer faster processing and lower costs, while larger models provide enhanced understanding and performance on complex tasks but require more resources. Evaluate your specific use cases and resource availability to help make an informed decision. 

SLM and LLM use cases

Carefully consider your specific use cases when comparing language models. Smaller models are ideal for tasks that require quick responses and lower computational costs, such as basic customer service chatbots or simple data extraction. On the other hand, large language models excel in more complex tasks requiring deep comprehension and nuanced responses, like advanced content generation or sophisticated data analysis. Aligning the model size with your specific business needs ensures you achieve both efficiency and effectiveness. 

SLM and LLM limitations

It’s also essential to consider limitations like computational requirements and scalability. Smaller models can be cost-effective and faster, but might not have the same nuanced understanding and depth of larger models. Larger models require significant computational resources, which can lead to higher costs and longer processing times. Balance these limitations against your specific use cases and available resources. 

This article touches on at-a-glance comparative information demonstrating the power and benefits of both SLMs and LLMs. With AI innovation accelerating at an intense pace involving different languages and scenarios, this rapid development will be sure to push the limits of both types of models—resulting in better, cheaper, and faster versions of current AI systems. This is particularly true for startups with limited resources where SLMs like Phi-3 open models will likely be the preferred, practical choice to leverage AI for their use cases.

Explore more resources on SLMs and LLMs

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• Read about the benefits of using SLMs in certain scenarios

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¹Small Language Models (SLMs): The Next Frontier For The Enterprise, Forbes.

²Small Language Models vs. Large Language Models: How to Balance Performance and Cost-effectiveness, instinctools.

³Big is Not Always Better: Why Small Language Models Might Be the Right Fit, Intel.

The post Explore AI models: Key differences between small language models and large language models appeared first on The Microsoft Cloud Blog.

Large language models (LLMs) are AI systems based on transformer architectures and trained on vast amounts of text data to understand and generate human-like text. Using deep learning techniques, LLMs process and produce accurate responses rapidly. Deep learning is a subset of machine learning that uses multi-layered neural networks to simulate the complex decision-making power of the human brain.

Large language models are trained on a massive volume of data, and once properly trained, they have a broad applicability for a range of natural language processing and machine learning applications. LLMs are typically multiple billions of parameters in size, making them five to ten times larger than small language models (SLMs).

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What can LLMs do?

Large language models (LLMs) offer significant benefits across various industries by automating and enhancing numerous tasks involving natural language processing. These AI-powered tools can rapidly analyze vast amounts of text data, generate human-like content, and provide intelligent responses to queries. However, always keep in mind that any content created by AI models and used in final deliverables must not infringe on copyrights or intellectual property rights of the original owners.

• In business, LLMs may improve customer service through chatbots, streamline document analysis, and assist with market research.
• In healthcare, LLMs may assist clinicians with reviewing medical literature and clinical documentation.
• In education, LLMs may help teachers create personalized learning materials and provide instant tutoring assistance for their students.
• In the legal industry, LLMs may help law firms with contract analysis and legal research.

Additionally, LLMs can help support content ideation for marketing, journalism, and creative industries.

Let’s take a brief tour through the world of large language models.

5 key features and benefits of LLMs

While there are many benefits of large language models, here are five to consider:

1. Natural language understanding

The model can interpret context, detect sentiment, and understand idiomatic expressions and colloquialisms. It can often infer unstated information and respond appropriately to ambiguous queries. Also, LLMs can combine information from various sources in their training data to answer complex questions, solve problems creatively, translate languages, and even assist in research and innovation.

Benefit: LLMs can comprehend context, nuance, and intent in the text that was input into it, which allows for more intuitive human-computer interaction. The large language model enables the discovery of new insights and connections across diverse fields. It also powers more intelligent search engines that provide direct, human-like answers to queries rather than just links to relevant pages.

2. Versatile multimodal generation

LLMs can produce coherent and contextually appropriate outputs in multiple styles, languages, and formats—from poems and stories to emails, technical reports, and even spoken language. With advancements in multimodality, these models now extend beyond text to support speech, images, and other forms of media. This facilitates global communication, broadens access to information, performs translation tasks, question-answering, generating code with minimal additional training, and even understanding code-switching within conversation or between different media types.

Benefit: Synthesizing knowledge across text, speech, and other modalities saves time and resources in content creation across various domains. The models can analyze and determine sentiment or emotional tone in both text and speech, which is valuable for market research, customer feedback reviews, and even personalized interactions like voice-based assistants or multimedia content generation.

3. Code generation and analysis

Large language models can produce code as well as text. For example, LLMs can assist developers by generating code snippets, functions, or even entire programs based on natural language descriptions. They can also analyze existing codebases to help identify bugs, suggest optimizations, and provide explanations of complex code sections, effectively serving as an AI-powered coding assistant. In addition, LLMs can assist developers with:

• Building applications
• Auto-completing code
• Finding errors in code
• Analyzing and debugging software code
• Offering round-the-clock assistance without fatigue
• Creating test cases based on function specifications
• Creating entire code blocks in various programming languages
• Suggesting appropriate design patterns for given problems
• Suggesting improvements for code readability and maintainability
• Identifying security issues across multiple programming languages

Benefit: Developers can tailor the code to specific industries and use cases, thus adapting the model to specialized domains like healthcare, law, marketing, customer service, scientific research, and finance.

4. Task-specific without fine-tuning

With their massive knowledge base, LLMs can perform tasks such as summarization, translation, question-answering, and code generation with minimal additional training. The LLMs can be retrained periodically to respond in a more human-like manner, incorporate new data, and improve performance. 

Benefit: Reduces the need for specialized models for different tasks since they are so capable. LLMs excel at generating content that sounds natural, across multiple subject areas, with high accuracy.

5. Scalability and efficiency

LLMs can process long-form content or analyze extensive documents in parallel, leveraging graphics processing unit (GPU) capabilities for faster training and inference. This allows for efficient handling of large-scale language tasks and rapid generation of responses.

Benefit: Easily handles increased workloads and adapts to growing business needs. They can analyze large volumes of text data to extract insights and patterns, aiding in decision-making processes and boosting productivity.

Use LLMs to build comprehensive AI solutions to revolutionize industries

LLMs have revolutionized natural language processing by offering robust capabilities for understanding and generating human-like text. Despite their significant advancements, there are still some limitations. To ensure their ethical and appropriate use across various sectors, continuous improvements are necessary as we move forward.

LLMs can be used with other Microsoft Azure AI products to build advanced and comprehensive solutions to suit most industries. Their features and benefits make them an attractive option for businesses seeking to enhance natural language processing capabilities across various applications—from customer service to content creation and software development.

The ability of large language models to understand context, generate coherent text, and adapt to specific domains makes them versatile and valuable tools that are not only applicable in fields beyond just language processing—such as software development, data science, decision support systems, and creative industries—but that organizations can rely on to boost productivity, efficiency, and innovation across sectors.

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At Microsoft, we have a long-standing practice of protecting our customers’ information. Our approach to responsible AI is built on a foundation of privacy, and we remain dedicated to upholding core values of privacy, security, and safety in all our generative AI products and solutions.

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The post 5 key features and benefits of large language models appeared first on The Microsoft Cloud Blog.

Researchers at The Center for Information and Language Processing in Munich, Germany found that “… performance similar to GPT-3 can be obtained with language models that are much ‘greener’ in that their parameter count is several orders of magnitude smaller.”² Minimizing computational complexity while balancing performance with resource consumption is a vital strategy with SLMs. Typically, SLMs are sized at just under 10 billion parameters, making them five to ten times smaller than large language models (LLMs).

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3 key features and benefits of SLMs

While there are many benefits of small language models, here are three key features and benefits.

1. Task-specific fine-tuning

An advantage SLMs have over LLMs is that they can be more easily and cost-effectively fine-tuned with repeated sampling to achieve a high level of accuracy for relevant tasks in a limited domain—fewer graphics processing units (GPUs) required, less time consumed. Thus, fine-tuning SLMs for specific industries, such as customer service, healthcare, or finance, makes it possible for businesses to choose these models for their efficiency and specialization while at the same time benefiting from their computational frugality.

Benefit: This task-specific optimization makes small models particularly valuable in industry-specific applications or scenarios where high accuracy is more important than broad general knowledge. For example, a small model fine-tuned for an online retailer running sentiment analysis in product reviews might achieve higher accuracy in this specific task than if they deployed a general-purpose large model.

2. Reduced parameter count

SLMs have a lower parameter count than LLMs and are trained to discern fewer intricate patterns from the data they work from. Parameters are a set of weights or biases used to define how a model handles and interprets information inputs before influencing and producing outputs. While LLMs might have billions or even trillions of parameters, SLMs often range from several million to a few hundred million parameters.

Here are several key benefits derived from a reduced parameter count:

• This significant reduction in size allows them to fit into limited-memory devices like smartphones, embedded systems, or Internet of Things (IoT) devices such as smart home appliances, healthcare monitors, or certain security cameras. The smaller size is cost effective too, because it means SLMs can be more easily integrated into applications without requiring substantial storage space or powerful server hardware.
• The lower latency leads to a quicker turnaround between input and output, which is ideal in scenarios such as real-time applications and environments where immediate feedback is necessary. Rapid responses help maintain user interest and can increase the overall experience with AI-powered applications.
• With fewer parameters to process, SLMs can generate responses much more quickly than their larger counterparts. This speed is crucial for applications that require real-time or near-real-time interactions, such as chatbots, voice assistants, or translation services.
• Low latency means queries are processed locally with near-instantaneous responses, making SLMs ideal solutions for time-sensitive applications like interactive customer support systems. Minimal on-device processing helps reduce the risk of data breaches, helps ensure information remains under organizational control, and aligns well with stringent data protection regulations, often found in the public sector as well as those proposed by the General Data Protection Regulation (GDPR). Plus, SLMs running at the edge helps ensure faster, more reliable performance, especially in scenarios where internet connectivity may be limited or unreliable. And devices with limited battery power or processing capabilities, such as low-end smartphones, can operate efficiently, thus extending their operational time between charges.

3. Enterprise-grade hosting on Microsoft Azure

Look for a small language model that provides streamlined full-stack development and hosting across static content and serverless application programming interfaces (APIs) that empower your development teams to scale productivity—from source code through to global high availability.

Benefit: For example, Microsoft Azure hosting for your globally deployed network enables faster page loads, enhanced security, and helps increase worldwide delivery of your cloud content to your users with minimal configuration or copious code required. Once your development team enables this feature for all required production applications in your ecosystem, we will then migrate your live traffic (at a convenient time for your business) to our enhanced global distributed network with no downtime.

Advantages of SLMs as efficient and cost-effective AI solutions

To recap, when deploying an SLM for cloud-based services, smaller organizations, resource constrained environments, or smaller departments within larger enterprises, the main advantages are:

• Streamlined monitoring and maintenance
• Increased user control over their data
• Improved data privacy and security
• Reduced computational needs
• Reduced data retention
• Lower infrastructure
• Functions offline

These features and benefits mentioned above make small language models such as the Phi model family and GPT-4o mini on Azure AI attractive options for businesses seeking efficient and cost-effective AI solutions. It is worth noting that these compact yet powerful tools play a role in democratizing AI technology, enabling even smaller organizations to leverage advanced language processing capabilities.

Choose SLMs over LLMs when processing specific language and vision tasks, more focused training is needed, or you are managing multiple applications—especially where resources are limited or where specific task performance is prioritized over broad capabilities. Because of their different advantages, many organizations find the best solution is to use a combination of SLMs and LLMs to suit their needs.

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Our commitment to responsible AI

Organizations across industries are leveraging Microsoft Azure OpenAI Service and Microsoft Copilot services and capabilities to drive growth, increase productivity, and create value-added experiences. From advancing medical breakthroughs to streamlining manufacturing operations, our customers trust that their data is protected by robust privacy protections and data governance practices. As our customers continue to expand their use of our AI solutions, they can be confident that their valuable data is safeguarded by industry-leading data governance and privacy practices in the most trusted cloud on the market today. 

At Microsoft, we have a long-standing practice of protecting our customers’ information. Our approach to responsible AI is built on a foundation of privacy, and we remain dedicated to upholding core values of privacy, security, and safety in all our generative AI products and solutions.

Learn more about Azure’s Phi model

• Learn more about the Phi model family.
• Read about how you can boost your AI with Azure’s new Phi model.
• Listen to the podcast on Phi-3 with lead Microsoft researcher Sebastien Bubeck.

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• Explore how customers are putting Microsoft AI to work.
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¹MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices, Cornell University.

²It’s Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners, The Center for Information and Language Processing in Munich Germany.

The post 3 key features and benefits of small language models appeared first on The Microsoft Cloud Blog.