Here’s what’s really going on inside an LLM’s neural network

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bugsbony

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I'm just a layman, but it keeps puzzling me why the question "why they often confabulate information" is considered relevant or interesting. It's just a matter of complicated statistics, what else could it be? What am I missing here? The network follows exactly the same process each time - whether the output ends up lining up with something that we can determine to be true (via external means) or whether it happens to end up lining up with something that we can determine to be false or nonsensical. It's not like the latter cases are caused by some bug or malfunction. Because at no point is there any process or capability invoked that goes beyond statistical relationships. There's no "truth" module, no "double-check" phase, no "how important is this" assessment, no way to suspend the statistics and employ some other approach that would be more suitable at some point.
It's quite simple really, they "confabulate" because that is what they were trained to do. And that is needed to not just repeat things but invent a response that best fits the rules it has learned. What is harder is tweaking the system so that they can learn to not do it when they shouldn't. It probably involves more than just improving the dataset or the alignment though.
But let me reassure you, "statistical relationships" is not the problem, that is how we learn.
 
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