I've been wondering, how do these large language models manage to give such accurate answers? Do they learn from datasets, or do they actually reason things out? I've been trying to visualize how they work, but I just can't quite wrap my head around it.
What do AI models base their responses on?
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I also used to think the same when I first learned about it, scratching my head with "How is this even possible?" Back then, I had even dropped a Python course halfway and later went through all the sample projects I found on GitHub. Months later, I thought I knew the code of those projects by heart — but in reality, I had just memorized what they did and how they did it from those documents. It's exactly the same: language models learn to generate patterns that "seem logical" from millions of texts. For now, they're just repeating patterns based on data, nothing more. Once I understood that, I felt both disappointed and amazed at how clever the whole thing is!