What Are Large Language Models?
A large language model (LLM) is a deep learning model designed to understand, translate, and generate humanlike language. LLMs are trained on enormous amounts of public domain data with millions or billions of parameters, which enables the text it generates to sound like a human wrote it.
LLMs are used within the broader domain of natural language processing (NLP), which is a branch of artificial intelligence (AI) that deals with the interaction between computers and human languages. NLP is used to analyze, understand, and generate human language, enabling machines to read and interpret text, speech, and other forms of communication.
LLMs serve as the foundational powerhouses behind some of today’s most used text-focused generative AI (GenAI) tools, such as ChatGPT, Google Bard, and Jasper. Much of the recent rise of and commercial investment in GenAI can be attributed to technological advancements in large language models, such as the availability of the transformer model architecture, new algorithmic innovations like attention mechanisms and optimization techniques, and the accessibility of open-source frameworks like TensorFlow and PyTorch.
Large Language Models vs. Generative AI
Speaking broadly, generative AI refers to AI solutions used to generate or create content—whether that’s written words, images, videos, or audio. LLMs are a specialized subset of GenAI that focuses on analyzing and creating text using natural language processing techniques.
LLMs often play a fundamental role in enabling GenAI solutions. Here, they’re used as a mechanism between the user and the AI model, enabling the system to understand the user’s input prompt and deliver an appropriate content output.
Why Are Language Models Important?
Large and small language models allow AI to interact with humans. They underpin many AI innovations like chatbots, AI assistants, and automated transcription services. LLMs enable AI systems to understand human intent and respond accurately.
Because LLMs are able to process massive amounts of written content much faster than humans, it makes them powerful tools for areas such as document review or legal research. Through this advanced analytic capability, LLMs can support enhanced decision-making and maximize productivity for a variety of use cases and industries.
Benefits of Large Language Models
Businesses that implement LLMs stand to gain numerous benefits:
- Streamlined operations: LLMs allow for the automation of repetitive, routine tasks, which helps boost employee productivity, improve efficiency, and lower costs.
- Accelerated innovation and product development: LLMs can surface important insights about consumer feedback and preferences and provide recommendations on how to improve existing products or whether new products are necessary.
- Business insights: NLP, which is powered by LLMs, can analyze and extract insights from unstructured business data quickly and accurately to allow companies to make data-driven decisions faster, automate repetitive tasks, and help identify opportunities for competitive advantage.
- Scalability and flexibility: LLMs can be scaled up to handle massive amounts of data, which means they can be used for multiple applications. Additionally, because LLMs are foundational models, they are a great starting point to build task-specific models through training and fine-tuning.
The benefits of LLMs extend well beyond businesses. Users also gain considerable benefits when LLMs are implemented at companies and LLM-based applications are readily available:
- Better user experience: LLMs can surface new insights and create more intuitive interfaces for products and services, making them easier for customers to use and understand.
- Improved customer service: LLMs can be used to create chatbots and virtual assistants that understand and respond to customer inquiries in a more natural language, improving customer service efficiency and effectiveness.
- Personalization recommendations: LLMs can analyze customer preferences and behavior and make personalized recommendations for products and services.
- Easier access to information: LLMs can make it easier for customers to find the information they need by allowing them to search for information using natural language queries.
Challenges of Large Language Models
While the use of LLMs brings considerable benefits to businesses and users, it also presents challenges and risks that cannot be overlooked:
- Biases: LLMs are trained on and learn from existing data that may hold biases. Therefore, there is potential that LLMs inherit those biases and propagate them in the subsequent text they generate.
- Environmental impact of training: Training massive LLMs requires substantial computational resources that can potentially leave a lasting, detrimental environmental impact. For example, research has shown training a single common LLM, such as Bidirectional Encoder Representations from Transformers (BERT) introduced by Google, on GPUs could emit as much CO2 as five cars would emit over their lifetime.1 Work is being done to reduce these impacts and make AI more sustainable as well as use AI to improve business sustainability efforts overall.
- Interpretability: It’s currently difficult to understand the LLM decision-making process and interpret how it arrives at the outputs it does. This is due to many factors, including the complex nature and sheer scale of LLMs, the size and diversity of datasets they are trained on, and the current lack of mature explainability tools. However, efforts in the AI community are underway to improve AI model transparency and explainability.
- Responsible use of AI: Additional challenges to using AI include ethical and societal implications. Leaders in AI innovation are collaborating on and committing to the pursuit of responsible AI practices that are transparent, inclusive, and accountable to help cultivate mindfulness about the potential impacts of AI on society and ensure that advances in AI continue to uplift communities.
How Large Language Models Work
Large language models use deep neural networks to process and generate text. They’re trained on sometimes millions or trillions of words to learn to find data patterns and structures to create new, humanlike text.
LLMs are based on a deep learning architecture called a transformer. Transformers allow the model to process input sequences in a parallel fashion, which improves performance and speed compared to traditional neural networks. Transformers are based on multilayers of self-attention mechanisms, which are key to enabling the LLM to process contextually relevant and coherent outputs. With self-attention mechanisms, the model is able to weigh the importance of different words in a sequence to record the relationship between them.
What Makes a Great Large Language Model?
Creating a high-quality LLM starts with the dataset it is exposed to and trained on. The more diverse and comprehensive the dataset, the better the LLM will be at generating contextually relevant and humanlike text.
A diverse and comprehensive training dataset typically extracts data from various sources on the internet, such as articles, websites, books, or other textual resources provided by the person or business developing the model.
One concern with sourcing training data from across the internet is that it presents the risk of the LLM generating misleading or biased text. Since an LLM learns based on the training data it is exposed to, if biased information is present, there’s a likely chance the LLM-generated text will inherit that bias.
Reinforcement learning from human feedback (RLHF) is a process that can help improve the quality of LLM responses. In RLFH, once the model generates a response, a human reviews the answer and scores its quality. If the answer is of low quality, the human creates a better answer. All human-provided answers are then fed back into the training dataset to retrain the model on what is a high-quality answer.
Additionally, the emergence and adoption of retrieval-augmented generation (RAG) is helping LLMs deliver more-accurate and relevant AI responses. In the RAG methodology, foundational large language models are connected to knowledge bases—often company-specific, proprietary data—to inject up-to-date, contextually relevant information.
How Large Language Models Are Trained
Training LLMs requires sizable volumes of data and considerable computing horsepower, especially for models that use many parameters. Depending on the intended use case for the LLM, it could be trained on a general-purpose dataset that includes a wide range of data or on a relatively small dataset that’s focused on a singular purpose or topic area. Oftentimes, organizations can start with a more general-purpose LLM, also known as a foundational model, and fine-tune it to suit their needs by training it on their unique, domain-specific data.
Training datasets for LLMs are typically massive collections of text that can include books, articles, websites, scientific research, and other written documents. These are used to help the model both understand human language and create accurate, humanlike responses. Typically, LLM training is carried out in an advanced data center environment, such as a public cloud or high-performance data center, that can provide extreme computational power in the form of specialized AI acceleration.
How Large Language Models Are Used
Large language models are used in a variety of ways by businesses, professionals, and everyday users. Popular LLMs, such as Generative Pre-trained Transformer (GPT) by OpenAI, have been trained on enormous and diverse datasets from the internet, which means they are often used to complete a wide range of tasks without task-specific training. Businesses can also fine-tune and implement LLMs to perform specialized, task-specific applications across industries.
Text Generation
LLMs can help improve productivity by generating initial drafts of many types of business and personal communications, such as emails, outlines, and marketing materials, in response to user prompts. Across many industries, LLMs are being used to enhance the customer experience by personalizing communications and marketing campaigns for greater engagement.
Summarizing Content
LLMs are often used to create summaries of long-form documents or collections of documents or data. For example, in healthcare, LLMs are used to process and analyze medical text, such as electronic health records, to generate reports for care providers on patient health or treatment patterns.
In education, GenAI powered by LLMs can yield unique insights for teachers and administrators with summaries analyzing student and class test and assignment performance.
Financial services organizations are using LLMs to analyze financial news articles and social media posts to identify sentiment and make predictions about stock prices.
AI Chatbots
LLMs allow AI chatbots to respond to user prompts in humanlike language.
Across industries, including manufacturing, banking, and the energy sector, companies are deploying GenAI-enabled chatbots, enterprise-specific personal assistants, and self-service portals to help increase customer support and satisfaction while reducing in-person calls to maximize employee time and increase efficiency.
Generating Code
By learning patterns from existing codebases and documentation, LLMs can be used to enhance developer productivity by producing code. Once trained on this information, LLMs can generate functions, classes, or entire programs based on natural language prompts or specifications.
Information Retrieval
Central to many LLM applications—including chatbots, customer service systems, and content summarization tools—is the ability to find and retrieve information from disparate systems in response to a query.
Popular search engines use LLMs to help improve the effectiveness and relevance of query responses. Employees at businesses with internal search engines connected to RAG databases can quickly search their company’s entire library of content assets to retrieve highly relevant resources they need.
Examples of Large Language Models
In the current ever-evolving AI landscape, examples of LLMs can be seen all around you—from the chatbot powering your banking to the AI assistant performing real-time translation via your phone.
Many of today’s most important LLMs are readily available for experimentation and integration. Some of the most popular models include:
- ChatGPT, an AI-powered conversational model by OpenAI, uses LLMs to provide information on a wide range of topics.
- BERT, a language model introduced by Google, is known for significantly changing how machines understand and process text.
- T5, or text-to-text transfer transformer, was developed by Google AI and is a powerful and versatile language model for text-based applications.
- Llama, developed by Meta AI, is a family of open source LLMs designed for scalability and efficiency.
- XLNet, a powerful language model developed by Google and Carnegie Mellon University, excels at natural language processing tasks using an innovative approach to pretraining.
Future of Large Language Models
Just as the future of AI technologies is evolving and rapidly changing, so too is the future of LLMs. Researchers are constantly exploring new ways to improve LLMs based on their current limitations and challenges. Here are some areas being focused on:
- Improving efficiency: As LLMs continue to grow in size, complexity, and capability, so too will their energy consumption. Researchers are developing ways to make them more efficient, thus reducing their computational requirements and the impact they have on the environment.
- Reducing bias: Researchers are taking a multifaceted approach to reducing bias since it’s a complex and ongoing challenge. This approach includes but is not limited to curating and diversifying datasets, forming industry and academia partnerships to share best practices and tools, conducting user studies and collecting feedback from diverse user groups to identify biases and iteratively refine models, and implementing techniques that detect and filter out biased content.
- Exploring new types of architectures: Large corporations are actively researching new LLM architectures, pretraining those models, and working to make them available for everyone to use and fine-tune.
Governing Large Language Models
LLMs require careful management of their development, deployment, and use to comply with regulatory guidelines and align with responsible AI principles.
Successful LLM governance demands the careful creation of frameworks, policies, and ethical guidelines. These governance policies can help organizations manage key considerations like ensuring only appropriate data is used for training, verifying model accuracy and transparency, and running all AI workloads on adequately protected infrastructure, whether in the cloud or on-premises. LLM governance also involves taking steps to ensure that the models deliver fair, equitable, and unbiased responses.