What are Large Language Models (LLMs)? Definition, use cases & examples

While Large Language Models (LLMs) have established themselves in the public consciousness and most digitally aware individuals have interacted with ChatGPT, many people are still unsure how the technology works or how it benefits us. There is a consensus in business that LLMs will radically alter how we work. But this often isn't backed up by specifics. Responding to this lack of information, our AI experts created this detailed introduction to LLMs, their use cases, and the many ways they will change the commercial landscape.

What is a Large Language Model (LLM)?

Large Language Models (LLMs) are machine learning models that can comprehend and generate human language text. These models are trained on vast amounts of text data gathered from a remarkably diverse array of digital sources, enabling them to interpret text and extract meanings and intent. This allows them to answer questions, summarize documents, translate languages, and generate content with greater accuracy than any previous technology.

While LLMs have gained significant traction in recent years, they are built on decades of AI development. Today, they are regarded as a highly disruptive technology that is redefining the way businesses and individuals leverage digital tools and reshaping how we think about processes in both the professional and personal spheres.

How do LLMs work?

Large Language Models are built on “deep learning” techniques that enable models to learn with a degree of autonomy (some human fine-tuning is typically required) through exposure to large amounts of text. Through this exposure, models begin to establish probabilistic relationships between the components of human language. This allows LLMs to predict the most appropriate response based on the inputs it receives.

In the context of Large Language Models, a specific type of neural network known as a “transformer model” underpins these abilities. They consist of many nodes spread over several layers. Each node is involved in processing information and passing it on to other nodes. The connections between nodes are weighted, helping the model determine an input's importance and predict the most appropriate response. Developers can improve the accuracy of this prediction by training the model.

Transformer models take this approach and apply a technique known as “self-attention.” This enables the neural network to consider the sequencing of various elements rather than just the elements themselves, helping LLMs understand how parts of an input relate to one another and provide additional context. In practical application, this ensures Large Language Models understand how the end of a sentence relates to its beginning or the relationship between a question and the paragraph in which it appears.

How are LLMs trained?

Developers need to train Large Language Models to understand and accurately respond to prompts and interpret text. Training involves feeding the models immense amounts of data so they can make predictions, check their accuracy, and then adjust the weightings they attribute to nodal connections. LLMs keep performing this process until their predictions are accurate. While there are elements of self-learning in this process, humans are involved in dataset curation, fine-tuning and evaluation, and there is considerable human oversight.

Once the training process is complete, developers can then fine-tune a Large Language Model, preparing it for specialist tasks and applications. This involves exposing the model to additional data relevant to the target application. For instance, LLMs intended for use in particular industries may need fine-tuning to accurately recognize and understand industry-specific jargon and context or adapt to a business's specific brand voice and tone. Alternatively, prompt engineering or in-context learning may be used to adapt the model without altering modal weights.

Use cases for a Large Language Model

LLMs have the potential to transform any task involving significant amounts of text, and as such, their applications are diverse.

Knowledge bases

Large Language Models can play a critical role in developing extensive knowledge bases that enable customers or employees to access information quickly and easily. LLMs can extract information from vast digital archives. They will revolutionize how we engage with stored data, giving us a new way to deliver customer support or assist human employees in their work.

Content summarization

Another use case for Large Language Models is summarizing long text documents, ranging from research articles to corporate or legal documentation, providing users with a concise round-up of key information. This would drastically speed up processes where identifying critical information or certain passages is highly beneficial.

Sentiment analysis

LLMs can help establish the tone or sentiment behind a text, giving businesses a more accurate understanding of how customers feel when interacting with their brand. Executed at scale, this gives businesses enhanced insight into customer satisfaction, helping them identify potential problem areas in the customer journey and make improvements.

Language translation

Translation is an increasingly popular use case for Large Language Models. LLMs are becoming increasingly proficient at translation tasks, enabling organizations to convert their documents, marketing materials, and other content into multiple languages quickly, accurately, and cost-effectively. This will improve efficiency and reduce costs for businesses operating in global markets.

Text generation

Large Language Models can generate text for any number of purposes. From copywriting how-to guides and developing marketing materials to writing contracts and emails, they streamline workflows and simplify content creation. Retrieval-augmented generation (RAG) further enhances these capabilities.

Code generation

LLMs are also being used to assist in code writing and development. While skilled developers still play a pivotal role in this process, LLMs are increasingly employed as assistants that can accelerate code generation based on natural language prompts.

The examples listed above comprise just a few of the many LLM use cases and applications. In practice, the technology has the potential to impact almost any text-based process.

Examples of LLMs

While the widespread availability of OpenAI’s GPT models has arguably made them the best-known LLMs in the world, they are by no means the only LLMs available. Anthropic’s Claude 2 LLM is sophisticated enough to compete with GPT, as is Google’s PaLM 2 model. Other notable examples of LLMs include AI21 Labs’ Jurassic-1 model, Cohere’s Command model, and LightOn's Paradigm model.

Benefits of Large Language Models for businesses

There can be no doubt that LLMs are fundamentally altering the way organizations go about their business, helping them to improve efficiency, develop new products and solutions, and deliver more satisfying customer experiences and support. Key benefits for businesses include:

Improved process efficiency

LLMs assist businesses in automating high-volume repetitive tasks that do not require human intervention. These tasks include processes such as data entry, document summarization, coding, and report generation, among others. By automating these tasks, LLMs streamline workflows and facilitate fast, accurate, data-driven decision-making.

Significant cost savings

At the same time, LLM automation removes the need for expensive human processing, reducing costs and helping businesses make savings. Employees no longer have to dedicate as much time to highly repetitive tasks. Instead, they can focus on work that requires greater nuance and maximizes their value to the organization. However, it is worth noting that implementing and maintaining Large Language Models often requires significant upfront investment, infrastructure, and skilled personnel. As such, cost savings will vary depending on the project scale and context.

Leveraging available data

Most businesses now recognize the importance of data in delivering products and services and improving customer satisfaction and loyalty. However, many do not know how to achieve this or do not have access to the tools required to leverage that data. One of the most significant benefits of LLMs is the ability to extract significant value from the data a business has collected. For instance, a company may have an extensive collection of technical documents containing critical product information that customers could use to troubleshoot issues. However, they may not have any means to retrieve that information or make it available to customers. LLMs resolve this issue.

Empowering employees

LLMs are now used to assist and empower employees, enabling them to work faster and more efficiently and ensuring they have all the information they need to make informed decisions. This support is invaluable when trying to improve employee productivity. Software development and coding are excellent examples. Using LLM assistants, developers can shortcut large parts of the coding process using natural language prompts, enabling them to bring products and solutions to market much sooner.

Better customer service

Large Language Models are drastically improving chatbot capabilities, making effective 24/7 customer support more viable and allowing customers to self-serve on a much larger scale. The technology’s ability to accurately interpret complex customer inputs means responses are more likely to resolve inquiries immediately, reducing customer service costs, improving customer satisfaction, and taking the strain off overwhelmed contact centers. They also allow for more personalized support and enable contact centers to handle increased demand during surges without hiring additional employees.

Finding that competitive advantage

Underwriting all these Large Language Model benefits is the idea that the technology enables businesses to find and exploit that competitive advantage over other companies in the sector. Whether you compete on customer experience, can gain the upper hand by increasing speed to market, or simply need to streamline processes to improve efficiency, LLMs may be the answer. However, their success depends on proper alignment with business goals and careful implementation.

AI technology is evolving at an astounding rate, and we are seeing new models emerge all the time. Looking to the future, we expect LLMs to become even more accurate and capable, with interactions becoming increasingly difficult to differentiate from authentic human-to-human ones.

However, we also expect the technology to become increasingly specialist or be applied in increasingly specialized ways. That may manifest as LLMs explicitly designed for medical applications with a better understanding of medical terminology and processes or LLMs developed to be incredibly effective multilingual translators. This move will be balanced by the fact that specialization risks making models less versatile and increases the need for domain-specific data, which may not always be readily available and can lead to higher costs. We are also likely to see other types of media integration, with audio and visual input increasing LLM capabilities and potential applications.