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Simply explained: how does GPT work?
(confusedbit.dev)
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this post was submitted on 23 Jul 2023
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If someone types the word "how are you?" into an SMS message box... chances are really high the other person will respond with "I'm good, how are you?" or something along those lines.
That's how ChatGPT works. It's essentially a database of likely responses to questions.
It's not a fixed list of responses to every possible question, it's a mathematical one that can handle arbitrary questions and deliver the most likely arbitrary response. So for example if you ask "how you are?" you'll get the same answer as "how are you?"
ChatGPT is also programmed to behave a certain way - for example if you actually ask how it is, it will tell you it's not a person and doesn't have feelings/etc. That's not part of the algorithm, that's part of the "instructions" OpenAI has given to ChatGPT. They have specifically told it not to imply that it is human or alive.
Finally - it's a little bit random, so if you ask the same question 20 times, you'll get 20 slightly different responses.
ChatGPT is not "impressively smart" at all. It's just responding with mathematically the most likely answer to every question you ask. It will often give the same answer as a smart person, but not always. For example I just asked it how far from my city to a nearby town, it said 50 miles that it'd take 2 hours to drive there. The correct answer is 40 miles / 1 hour.
I expect there's probably a whole bunch of incorrect information in the training data set — it's a popular tourist drive, and tourists probably would take longer, stop along the way, take detours, etc. For a tourist, ChatGPT's answer might be correct. But it's not because it's smart, it's just that's what the algorithm produces.
I think calling it just like a database of likely responses is too much of a simplification and downplays what it is capable of.
I also don't really see why the way it works is relevant to it being "smart" or not. It depends how you define "smart", but I don't see any proof of the assumptions people seem to make about the limitations of what an LLM could be capable of (with a larger model, better dataset, better training, etc).
I'm definitely not saying I can tell what LLMs could be capable of, but I think saying "people think ChatGPT is smart but it actually isn't because <simplification of what an LLM is>" is missing a vital step to make it a valid logical argument.
The argument is relying on incorrect intuition people have. Before seeing ChatGPT I reckon if you'd told people how an LLM worked they wouldn't have expected it to be able to do things it can do (for example if you ask it to write a rhyming poem about a niche subject it wouldn't have a comparable poem about in its dataset).
A better argument would be to pick something that LLMs can't currently do that it should be able to do if it's "smart", and explain the inherent limitation of an LLM which prevents it from doing that. This isn't something I've really seen, I guess because it's not easy to do. The closest I've seen is an explanation of why LLMs are bad at e.g. maths (like adding large numbers), but I've still not seen anything to convince me that this is an inherent limitation of LLMs.
Agreed, smartness is about what it can do, not how it works. As an analogy, if a chess bot could explore the entire game tree hundreds of moves ahead, it would be pretty damn smart (easily the best in the world, probably strong enough to solve chess) despite just being dumb minmax plus absurd amounts of computing power.
The fact that ChatGPT works by predicting the most likely next word isn't relevant to its smartness except as far as its mechanism limits its outputs. And predicting the most likely next word has proven far less limiting than I expected, so even though I can think of lots of reasons why it will never scale to true intelligence, how could I be confident that those are real limits and not just me being mistaken yet again?