What Are Large Language Models (LLMs)?

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.

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.

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 Used

Large language models are used in a variety of ways by businesses, professionals, and everyday users. Popular LLMs, such as GPT (Generative Pre-trained Transformer) 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, such as
 

• answering questions
• summarizing documents or texts
• interpreting tables and charts
• generating creative content, like stories or poems
• translating languages

Businesses can also fine-tune and implement LLMs to perform specialized, task-specific applications across industries like:
 

• Automotive: LLMs are an essential component in creating next-generation vehicles that employ GenAI assistants for drivers and passengers.
• Customer service: LLMs are used to automate aspects of customer service. For example, businesses can implement chatbots that can understand and respond to customer inquiries in humanlike language. This can reduce response time, increase efficiency, and improve customer satisfaction.
• Education: GenAI powered by LLMs in education is being used to personalize content, deliver near-real-time feedback, and guide coaching and skills development.
• Energy: GenAI powered by LLMs is being used in the energy sector to enable more empathetic customer experiences with chatbots and provide enterprise-specific personal assistants; simulate and generate optimal grid configurations, test various demand scenarios and outage response strategies, and plan the integration of new energy sources; and to ingest and analyze data from a wider variety of sources for advanced analytics use cases in support of predictive maintenance.
• Financial services and banking: LLMs are widely used in banking and financial services to process large amounts of transactional data to help detect and prevent fraud and mitigate risk. They are also used to analyze financial news articles and social media posts to identify sentiment and make predictions about stock prices, as well as to deploy AI chatbots and financial assistants for customers.
• Government: GenAI powered by LLMs is being used in government agencies to create personalized AI chatbot experiences with the ability to understand the user’s needs better and provide more contextual information, as well as to enable automation and informed decision-making in the office, laboratory, and field.
• Healthcare: In healthcare, LLMs are used to process and analyze medical text, such as electronic health records, to extract important information and improve patient care. They can also generate reports or offer medical treatment suggestions.
• Manufacturing: GenAI-enabled chatbots and self-service portals are helping increase customer support while reducing in-person calls to maximize employee time. LLMs are also used to enhance the customer experience by personalizing communications, marketing campaigns, and emails for greater engagement.
• Media and entertainment: LLMs are used to analyze large amounts of content and data to make personalized recommendations, improve content creation, and better understand audience behavior.

Challenges of Large Language Models

While the use of LLMs brings considerable benefits to businesses and users, they also present 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 as 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.

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.