What is a “Large Language Model”? LLM Explained Using ChatGPT

You’ve probably heard that ChatGPT is a Large Language Model (LLM)—but really what is a “Large Language Model”? What does that mean?

For many, the term LLM is just another piece of trendy jargon tossed around when talking about AI. But not for you. Understanding what an LLM is and how it works can give you a glimpse into the genius behind ChatGPT (as well as Artificial Intelligence as a whole). You might be amazed at how far humans have come in developing AI that can assist us, understand us, and communicate with us almost like another person.

Let us dissect what an LLM is for you by answering 7 key questions about it. Whenever possible, we’ll use ChatGPT as an example to help explain these concepts.

1. What is an LLM?

A Large Language Model (LLM) is an artificial intelligence model that is designed to understand, generate, and interact with human language. It’s called “large” because it’s trained on massive amounts of text data and parameters…

…And we’re talking about billions of “parameters”—which, essentially, are the adjustable settings that allow the model to predict words, understand context, and generate responses.

The purpose of an LLM is to mimic a human-like understanding of language, so that it can perform tasks like answering questions, holding conversations, generating text, as well as compiling and presenting data to its users.

Example: ChatGPT
ChatGPT is a prime example of an LLM in action. It uses the GPT (Generative Pre-trained Transformer) architecture, which allows it to understand input from users in a conversational manner and generate human-like responses.

Think back to less than a decade ago, when the best we could do when communicating with a computer system was to use its language. Although these systems were often designed to resemble human language, they weren’t exactly natural human language—we had to master the specific syntax and semantics unique to each one.

Now, when we chat with ChatGPT, it feels almost like talking to another person, thanks to the LLM technology running behind the scenes.

2. How do LLMs work?

LLMs work by using neural networks—specifically, a type of architecture known as Transformers. Transformers are designed to handle sequences of data, like sentences, by considering all the words in the sentence simultaneously and understanding their relationships, rather than processing them one word at a time.

The key innovation is the LLM’s ability to understand the overall meaning of a sentence by figuring out which words and phrases in the sentence are most important.

Example: ChatGPT
When you type a question into ChatGPT, it doesn’t just look at your words one by one. Instead, it analyzes your entire sentence, focusing on the most relevant parts to generate a meaningful response. This is why ChatGPT can keep track of context and respond in a way that feels coherent and natural.

3. How are LLMs trained?

Vast amounts of text data—from books and articles to websites and conversations—were fed into LLMs to train them. During training, the model learns to recognize patterns in language, such as grammar, word meanings, and sentence structure.

The goal of this training is to adjust the model’s internal parameters over millions of steps, to refine its ability to provide accurate and relevant responses while minimizing errors. The training also expands the LLM’s knowledge base and updates its information, enabling it to stay relevant to recent events (although this is still pretty restricted by the knowledge cutoff dates) and respond to a broader range of topics.

Example: ChatGPT
ChatGPT was trained on a diverse dataset that included a wide range of internet text. This training helps it learn how people communicate, allowing it to generate responses that sound natural and make sense in context. However, it’s important to note that ChatGPT doesn’t “know” things in the way humans do—it just predicts what should come next based on patterns it has seen before.

4. What are LLMs used for?

LLMs have a broad range of applications due to their versatility and ability to handle various language tasks. They are used in chatbots, virtual assistants, content generation, language translation, text summarization, and much more. Essentially, any task that involves understanding or generating text can benefit from LLM technology.

Example: ChatGPT
You might have instinctively thought of ChatGPT as you read the above paragraph. Think about how we’ve used it to produce content, write emails, brainstorm ideas, assist with research… or even as a chat partner when we’re bored or need emotional support.

5. What are the limitations of LLMs?

Despite seeming incredibly powerful and all-knowing, LLMs do have notable limitations.

Will you be surprised if we told you that LLMs do not truly understand the information they process? LLMs merely recognize and reproduce patterns from their internal data. This can lead to incorrect or misleading responses, especially if the model is asked about topics it wasn’t well trained on.

Additionally, they can reflect biases present in their training data, sometimes producing inappropriate or prejudiced outputs.

LLMs are also notorious for hallucinating—confidently throwing out false or nonsensical information as if it is accurate. For instance, ChatGPT was caught citing a non-existent study as a source. Consequently, users may be misled when they rely on this information without taking the time to verify it.

Example: ChatGPT
ChatGPT exhibits all the limitations mentioned above. In a separate blog post, we discuss these, along with other well-known limitations of ChatGPT, in detail.

The best advice we can offer—and have emphasized throughout this website—is to maintain a healthy dose of skepticism when using ChatGPT and other AI tools. It’s up to us, the users, to perform due diligence and carefully verify ChatGPT’s outputs.

6. What are some popular LLMs?

Here are some popular LLMs:

7. Why are LLMs important?

LLMs have transformed the landscape of AI by making it possible to automate and enable complex language tasks that were previously difficult for machines. Take content creation, for example. Before LLMs, computer programs struggled to generate coherent, contextually relevant, and engaging text because they lacked the ability to understand subtle linguistic details. This often resulted in outputs that were repetitive, bland, or nonsensical.

As LLMs continue to advance, their roles will become increasingly important in fields like communication, research, productivity improvement, information accessibility, marketing… and everything from business operations to personal interactions.

Example: ChatGPT
In just two years since its launch, ChatGPT has demonstrated its unmatched competence in providing immediate assistance, creative support, and personalized responses to its users. It will only become more and more powerful and reliable as it continues to advance.

Conclusion

Understanding Large Language Models, from their training and architecture to their applications and limitations, is key to appreciating how tools like ChatGPT work. By using ChatGPT as an example, we have seen how these models function and why they have become such a significant part of modern digital interactions.

LLMs (or should we say “artificial intelligence”) are more than just text generators and chat partners—they’re transforming how we accomplish everyday and professional tasks and will only become more integral to our lives. Keep an eye on how they advance!