The Breakdown: What is a large language model (LLM)?

Generative AI (GenAI) has quickly gone from being a tech buzzword to a mainstream technology, used by individual consumers and corporate enterprises. From creating astonishingly realistic images to handling customer support and search engineer interactions, the potential for innovations in GenAI has captured our imagination.

At the heart of GenAI’s transformative power is the large language model—or LLM for short.

What is an LLM?

An LLM is an artificial intelligence (AI) system capable of understanding, generating, and interacting with human languages in ways that were once thought impossible. By analyzing vast datasets of text, LLMs learn to mimic language patterns, make predictions, and generate content that feels surprisingly and almost unmistakably human. LLMs are the technology behind your helpful chatbot assistants, sophisticated content creation tools, and much more.

To break down what you need to know about LLMs, we’ll cover the core concepts that are foundational to LLMs followed by a simple discussion of how LLMs work. Familiarity with this technology is important, whether you’re a decades-long veteran in tech or just curious about how it’s shaping our future.

Key concepts to know

Before diving deeper into how LLMs work, it's essential to understand the key technologies they're built upon.

Artificial intelligence (AI)

AI is the broader field that encompasses technologies designed to mimic human intelligence. This includes being able to:

• Learn
• Understand language
• Recognize patterns
• Make decisions
• Solve problems

AI can range from simple algorithms that perform specific tasks to complex systems that can understand and interact with the world in a human-like manner. LLMs are a specific and sophisticated form of AI specialized in handling and generating human language.

Machine learning (ML)

ML is a subset of AI focused on enabling computers to learn from data and improve their performance of tasks over time—without being explicitly programmed for every task. It involves feeding large amounts of data into algorithms and allowing these systems to adjust and improve based on the patterns and information they extract. LLMs use ML to analyze vast volumes of text to learn the nuances of language from this data.

Neural networks

Neural networks are a type of computing that consists of layers of interconnected nodes (or “neurons”) that process data and can learn complex patterns through training. Their formation is inspired by the structure and function of the human brain. By mimicking the way humans think and learn, neural networks enable LLMs to understand and generate language.

Natural language processing (NLP)

NLP is a field of AI focusing on the interaction between computers and humans through language. It involves teaching machines to understand, interpret, and generate human language in a way that is both meaningful and useful. LLMs are an application of NLP, with an ability to process natural language inputs and produce outputs that humans can understand and engage with effectively.

Transformer models

The underlying architecture of current state-of-the-art LLMs is an architecture called the transformer model. Transformer models excel at understanding the context of language by paying attention to the relationship between all words in a sentence or document, regardless of their position, rather than considering words one at a time.

For example, consider the following sentence: “To get to the bank, the man needed to cross the river.” In this sentence, the word “bank” could refer to the side of a river or a financial institution. Without clear, nearby clues, traditional language models might struggle to determine the correct meaning of the word. However, a transformer model can consider the entire sentence, noticing that “cross the river” suggests “bank” refers to a riverbank. Broadening its attention even further, the transformer model may recognize clues from surrounding sentences or pages to provide additional context.

Training data

Training data is the collection of information used to train ML models. The training data for LLMs comprises vast amounts of text sourced from books, articles, websites, and other digital content. By analyzing this data, LLMs learn language patterns, grammar, context, and even cultural nuances. This enables them to generate coherent and contextually relevant text.

With these core concepts in mind, let’s break down how LLMs work.

Breaking it down: How LLMs work

Imagine you're at the world's largest library, and there's a librarian who has not only read every single book in the library but also remembers every word. You can ask this librarian any question, and they'll give you an answer by piecing together everything they've learned. Essentially, an LLM performs a very similar function. It sifts through the vast amounts of text it has been trained on—much like our extraordinary librarian—to provide responses that are relevant, informative, and sometimes even creative.

An LLM is much more than a search engine. A search engine returns a list of results (such as web links or document excerpts) based rigidly on your simple query. However, an LLM can understand the nuances of your question and grasp the context of what you’re asking. After finding what you’re looking for, an LLM crafts a complete, coherent response in natural language. That’s why using an LLM is much more akin to having a conversation with a friendly, knowledgeable librarian.

Training LLMs 

Ultimately, an LLM accepts a natural language input, searches through its vast repository of knowledge, and then produces a natural language response. Its ability to do this comes down to its training, which is a big project taken on by data scientists and computer programmers. They gather a huge amount of written material, including books, articles, and websites, to give the LLM as much information as possible about how we use language. The quantity and diversity of data helps the LLM understand different ways people communicate, from casual chats to expert discussions.

As the LLM trains on this data, its learning process focuses on spotting patterns in the text and getting the hang of how words and sentences are put together. Think of it as teaching the LLM to predict what comes next. This task requires powerful computers because of the sheer amount of information the LLM needs to learn from. After all this work, the LLM can generate text that sounds like it was written by a human, all thanks to the diverse and extensive training it received.

Real-world examples of LLMs 

After an LLM is trained, the magic happens through user-friendly apps and tools built to work with it. We call these GenAI applications, and they serve as bridges between us and the underlying LLMs. You ask a question or give a command, and the app brings your prompt to the LLM to work with. The LLM returns a human-like response or a piece of content that the app serves back to you.

With LLMs in the picture, everyday tech takes a giant leap forward. Imagine waking up to a virtual assistant that not only summarizes the top news stories but also offers a brief overview of your day ahead, merging information from your calendar with real-time traffic updates. And it does all this in a way that feels like a friend filling you in. This isn't about “Hey Alexa” commands anymore; it's about engaging, meaningful interactions that make your day easier and more informed.

In customer support, user experience is transformed just as profoundly. Instead of scripted responses, you encounter support that truly understands the essence of your query and the context behind it. Whether you’re troubleshooting a kitchen appliance or inquiring about a pharmacy prescription, you receive personalized assistance, making it feel like the system genuinely recognizes your needs.

Why LLMs matter

LLMs and the GenAI applications built on top of them are revolutionizing the way we interact with technology, making it more intuitive, personalized, and efficient. By understanding and generating human language in a way that feels natural, they're opening new possibilities for automation, improving accessibility to information, and ushering in a new era of innovation and creativity.

Acknowledging the challenges of LLMs 

LLMs are powerful, but they're not perfect. It's important to remember that an LLM's knowledge is frozen at the point when its training ends. It does not have any information regarding events that happened after that training cutoff date. However, new techniques (such as retrieval-augmented generation) are being introduced to enhance LLMs with the up-to-date information it might be missing. 

Another challenge of LLMs is a phenomenon called AI hallucination. This happens when an LLM makes up information or gives an answer that sounds convincing but isn’t actually true or based on real facts. Hallucinations occur because the LLM is trying its best to provide a relevant answer based on the patterns it has learned, even when it doesn't have accurate information to draw from. In addition, if an LLM’s training data had mistakes or biases, these issues can show up in the LLM’s responses, sometimes spreading incorrect or unfair information. 

LLMs are constantly improving in their effectiveness and human likeness. While this is incredibly promising, it also has implications. As LLMs get better and better at sounding like humans, how will you be able to tell if what you're reading was written by a person or a computer? This raises big questions about trust and authenticity in the information we consume daily. 

Navigating the complexities of LLMs reveals a landscape where innovation meets responsibility. As we embrace the advancements that LLMs offer — from making our interactions with machines more natural to automating tasks that free up our time for creativity — we also confront the challenges they pose.  

As modern enterprises build GenAI applications with LLMs, Outshift helps them move forward with a keen eye on improving accuracy, fairness, and transparency, ensuring LLMs serve as a force for good. For more information, read more about how Outshift leads the charge toward trustworthy and responsible AI.