5 key features and benefits of large language models

What are large language models (LLMs)?

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).

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.

Our commitment to responsible AI

Organizations across industries are leveraging 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.

Get started with Azure OpenAI Service

• Explore Azure Open AI Service.
• Watch this video to learn more about the Azure AI model catalog.
• Read about how you can Deploy large language models responsibly with Azure AI.

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• Learn how to build and optimize your strategic plan for AI with the AI Strategy Roadmap.
• Read An Introduction to Generative AI and Safety.