LLMs explained in a way that everyone can easily understand
In the world of business and artificial intelligence, there’s much chatter these days around something called LLMs. But there’s also a lot of confusion about what exactly are they, and how they work. If you’re not a tech guru or an AI enthusiast, you might feel like you’ve been handed a Rubik’s Cube with your eyes closed. Not to worry! We’re here to explain all in simple, everyday terms.
What is all the chatter about?
LLM stands for ‘Large Language Model’. Think of it as a supercharged version of predictive text on your smartphone—on a scale that’s unimaginably massive and much cleverer. While your phone might guess the next word you want to type, an LLM can generate entire essays, hold conversations, write poetry and even help with coding. These models are like the bookworms of the digital age, having read and memorised vast numbers of books, articles and websites to understand language and produce coherent text. In fact, the very tool generating this blog post, GPT-4, is a prime example of a powerful LLM at work.
How does an LLM work?
To understand how they work, let’s imagine training an LLM as being similar to teaching a parrot to speak. You give the parrot a lot of examples of things to say (data), and over time, it learns to mimic these phrases. But unlike a parrot, which repeats phrases exactly as it heard them, an LLM has the ability to remix and create new sentences it has never encountered before. Here’s how the magic happens:
- Training data: Just like a parrot needs to hear language to learn it, LLMs need exposure to text. They are fed vast amounts of written material from books, websites and more. This is no small reading list; it’s more like every book in the library, plus all the magazines and newspapers published in the past 200 years. GPT-4, for instance, has been trained on uncountable terabytes of text data, not just the whole of Wikipedia, but the entire internet. This training helps it learn the structure of different types of text, and so it knows the difference between a formal essay and a casual chat.
- Patterns and predictions: As an LLM consumes all this text data, it starts to identify patterns in how words and phrases are used. It uses something called ‘tokens’, which are essentially bits of words or characters. By analysing these tokens, the model learns what word might come next in a sentence. For example, if you type “The sun rises in the…”, an LLM trained on millions of examples knows the most likely next word is “east.” It learns these patterns by adjusting billions of internal settings, called parameters, to fine-tune its understanding of language.
- Creating content: When you ask an LLM a question or give it a task (like writing a blog post), it uses the patterns it has learned to generate a response. This process involves selecting the most probable next word, one after the other, until it forms a complete sentence or paragraph. If you ask it to write a story about a knight and a dragon, it doesn’t recall a specific story it read. Instead, it draws on all the narrative patterns it knows to create a new, unique story.
- Fine-tuning: Some LLMs are given additional training to specialise in certain areas, like medical information or legal advice. This is like giving our parrot some extra lessons on specific subjects so it can talk about more than just everyday topics. For instance, a model might be fine-tuned with medical journals to help provide health advice or with legal documents to assist with contract drafting.
Why are LLMs useful?
You might be wondering, why go to all this trouble? Well, LLMs have a wide range of uses:
- Content Creation: LLMs can help write articles, create marketing copy, or even draft emails. They’re like having a tireless assistant who never runs out of ideas. For instance, an LLM could help a company generate blog posts on the latest tech trends, or come up with engaging social media captions that align with a brand’s voice.
- Customer Service: They can chat with customers, answering common questions and solving problems. Imagine a help desk that’s available 24/7, never gets tired, and can handle multiple queries simultaneously. An online store could use an LLM to handle returns and refunds queries without any human intervention, improving response times and customer satisfaction.
- Language Translation: LLMs can translate text from one language to another, making communication across different languages easier and more accessible. This isn’t just about word-for-word translation; LLMs can understand context, so they can provide more natural and accurate translations. Imagine translating a business proposal from English to Spanish with nuances preserved.
- Programming Assistance: Some LLMs are trained to help with coding, offering suggestions and even writing snippets of code, making life easier for software developers. A developer could use an LLM to help write Python scripts by simply describing what they need, and the LLM would generate the code.
There are many more uses, and since this technology is barely in its infancy, imagine what will be possible when it comes of age and people have had the chance to play with it for a while.
It’s not all plane sailing
Although LLMs are very impressive tools, they aren’t perfect. They occasionally provide incorrect information because they generate responses based on patterns rather than true understanding. For example, an LLM might confidently provide outdated information if its training data isn’t up-to-date. It’s therefore always a good idea to have a human double-check all the important stuff!
Also, LLMs require substantial computational resources to train, which is costly. Still they enable people to save money elsewhere and ongoing advancements will make them more efficient and accessible.
So, now you know
Large language models are like highly trained digital parrots that have read more books than a human could in a thousand lifetimes. They predict and generate text based on patterns they’ve learned from a massive amount of data. LLMs like GPT-4 are already transforming industries by assisting in content creation, customer service, market analysis and more.
Here at Big Black Point, we leverage the power of LLMs to help businesses create high-quality content that engages and resonates with their customers, prospects and employees. Content like this blog post. So, next time someone mentions LLMs, you’ll know they’re talking about intelligent, highly-trained parrots—and you read all about them here.