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>> And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers. I thi
by ebcode 3y ago
>> And the problem is more - how can an LLM tell us it doesn't know something instead of just making up good sounding, but completely delusional answers.
I think the mistake lies in the belief that the LLM "knows" things. As humans, we have a strong tendency to anthropomorphize. And so, when we see something behave in a certain way, we imagine that thing to be doing the same thing that we do when we behave that way.
I'm writing, and the machine is also writing, but what I'm doing when I write is very different from what the machine does when it writes. So the mistake is to say, or think, "I think when I write, so the machine must also think when it writes."
We probably need to address the usage of the word "hallucination", and maybe realize that the LLM is always hallucinating.
Not: "When it's right, it's right, but when it's wrong, it's hallucinating." It's more like, "Sweet! Some of these hallucinations are on point!"
- p1esk 3y agoI think when I write, so the machine must also think when it writes. What is it exactly you do when you “think”? And how is it different from what LLM does? Not saying it’s not different, just asking.
- Jensson 3y agoThere are probably many, but the most glaring one is that LLMs has to write a word every time it thinks, meaning it can't solve a problem before it starts to write down the solution. That is an undeniable limitation of current architectures, it means that the way the LLM answers your question also matches its thinking process, meaning that you have to trigger a specific style of response if you want it to be smart with its answer.
- p1esk 3y agoOk, so how do humans solve a problem? Isn’t it also a sequential, step by step process, even if not expressed explicitly in words? What if instead of words a model would show you images to solve a problem? Would it change anything?
- dkjaudyeqooe 3y agoNo, I don't know how other people think but I just focus on something and the answer pops into my head. I generally only use a step by step process if I'm following steps given to me.
- pixl97 3y ago>but I just focus on something and the answer pops into my head. It's perfectly valid to say "I don't know", because no one really understand these parts of the human mind. The point here is saying "Oh the LLM thinks word by word, but I have a magical black box that just works" isn't good science, nor is it a good means of judging what LLMs are capable or not capable of.
- deleted 3y ago[deleted]
- ebcode 3y agoThat's a difficult question to answer, since I must be doing a lot of very different things while thinking. For one, I'm not sure I'm never not thinking. Is thinking different from "brain activity"? We can shut down the model, store it on disk, and boot it back up. Shut down my brain and I'm a goner. I'm open to saying that the machine is "thinking", but I do think we need more clear language to distinguish between machine thinking and human thinking. EDIT: I chose the wrong word with "thinking", when I was trying to point out the logical fallacy of anthropomorphizing the machine. It would have been more clear if I had used the word "breathing": When I write I'm breathing, so the machine must also be breathing.
- p1esk 3y agoI don’t think that “think” is a wrong word here. I believe people are machines - more complicated than GPT4, but machines nevertheless. Soon GPT-N will become more complicated than any human, and it will be more capable, so we might start saying that whatever humans do when they think is simpler or otherwise inferior to what the future AI models will do when they “think”.