So, for example, a bot might not always choose the most likely word that comes next, but the second- or third-most likely. Push this too far, though, and the sentences stop making sense, which is why LLMs are in a constant state of self-analysis and self-correction. Part of a response is of course down to the input, which is why you can ask these chatbots to simplify their responses or make them more complex.
Human beings are involved in all of this too : Trained supervisors and end users alike help to train LLMs by pointing out mistakes, ranking answers based on how good they are, and giving the AI high-quality results to aim for. Technically, it's known as “reinforcement learning on human feedback” . LLMs then refine their internal neural networks further to get better results next time.
As these LLMs get bigger and more complex, their capabilities will improve. We know that ChatGPT-4 has100 trillion parameters, up from 175 million in ChatGPT 3.5—a parameter being a mathematical relationship linking words through numbers and algorithms. That's a vast leap in terms of understanding relationships between words and knowing how to stitch them together to create a response.
From the way LLMs work, it's clear that they're excellent at mimicking text they've been trained on, and producing text that sounds natural and informed, albeit a little bland. Through their “advanced autocorrect” method, they're going to get facts right most of the time. But it's here where they can start to fall down: The most
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