But you’re not dealing with a person.
These chatbots don’t actually understand the meaning of words the way we do.
Instead, they’re the interface we use to interact with large language models, or LLMs.
The makers of generative AI tools are constantly refining their LLMs' understanding of words to make better predictions.
We’re now several generations into the evolution of LLMs.
OpenAI introducedGPT-4oin May,GPT-4o Miniin July andOpenAI o1in September.
Google has variations includingGemini 1.5 Pro and 1.5 Flash.
Meta is now atLlama 3, while Anthropic is up toClaude 3.5.
If you’re wondering what LLMs have to do with AI, this explainer is for you.
What is a language model?
you might think of a language model as a soothsayer for words.
“What makes something a language model is whether it can predict future words given previous words.”
This is the basis of autocomplete functionality when you’re texting, as well as of AI chatbots.
What is a large language model?
A large language model contains vast amounts of words, from a wide array of sources.
These models are measured in what is known as “parameters.”
What’s a parameter?
The number of variables in these computations are parameters.
A large language model can have 1 billion parameters or more.
Is there such a thing as a small language model?
What’s under the hood of a large language model?
(The artificial neurons in neural networks mimic the behavior of the neurons in our brains.)
How do large language models learn?
LLMs learn via a core AI process called deep learning.
Tokens help AI models break down and process text.
you’re able to think of an AI model as a reader who needs help.
From there, the LLM can analyze how words connect and determine which words often appear together.
“It’s like building this giant map of word relationships,” Snyder said.
LLMs also learn to improve their responses through reinforcement learning from human feedback.
“And then you’ve got the option to teach the model to improve its responses.”
What do large language models do?
Given a series of input words, a LLM will predict the next word in a sequence.
In other words, each word sets up context for what should come next.
What do large language models do really well?
LLMs are very good at figuring out the connection between words and producing text that sounds natural.
Where do large language models struggle?
But they have several weaknesses.
First, they’re not good at telling the truth.
Those are known as hallucinations.
“They’re not trained or designed by any means to spit out anything truthful.”
They also struggle with queries that are fundamentally different from anything they’ve encountered before.
That’s because they’re focused on finding and responding to patterns.
A good example is a math problem with a unique set of numbers.
They also don’t interact with the world the way we do.
How will large language models evolve?
This means they could better understand queries and provide responses that are more timely.
That was the goal, for instance, withAI-powered Bing.
But there are catches.
Web search could make hallucinations worse without adequate fact-checking mechanisms in place.
And LLMs would need to learn how to assess the reliability of web sources before citing them.
Google learned that the hard way with theerror-prone debut of its AI Overviews search resultsearlier this year.
The search company subsequentlyrefined its AI Overviews resultsto reduce misleading or potentially dangerous summaries.
Meanwhile, models includingGoogle’s LumiereandOpenAI’s Soraare even learning to generate images, video and audio.
