Why have artificial intelligence (AI) chatbots like OpenAI’s ChatGPT, Google’s Bard, and Microsoft’s Bing become so sophisticated and usable? There’s a simple explanation: Large Language Models (LLMs).
LLMs have only been around for about five years, but recently these deep learning algorithms have advanced so rapidly that you can hold natural, human-like conversations with chatbots and actually get value from them.
In fact, even though ChatGPT — the flag-bearer for this technology — only came out in November 2022, more than half of US Internet users have tried it. Granted, most folks are just experimenting. But many others are finding value in these tools for research, generating ideas, and crafting remarkably proficient essays, articles, resumes, and emails. And Wall Street has been going gaga over anything even remotely AI related, in part, because of what LLM technology makes possible.
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How it works
To understand how LLMs work, think about them like black holes. The more matter they consume, the bigger and stronger they become. LLMs have similarly insatiable appetites — for data. The more they’re fed, the better they function, which is why they are called “large” language models.
LLMs are built on neural network “transformers,” which detect how words relate to one another in order to process and understand natural language inputs and spit out human-like responses. But of course, as with any AI, you have to “train” the model to be able to access, understand, and transform data. The more learnable “parameters,” or variables, your LLM has for finding patterns in data, the more advanced its reasoning capabilities will be.
