Tech experts are starting to doubt that ChatGPT and A.I. ‘hallucinations’ will ever go away: ‘This isn’t fixable’::Experts are starting to doubt it, and even OpenAI CEO Sam Altman is a bit stumped.
Tech experts are starting to doubt that ChatGPT and A.I. ‘hallucinations’ will ever go away: ‘This isn’t fixable’::Experts are starting to doubt it, and even OpenAI CEO Sam Altman is a bit stumped.
Sure, but these things exists as fancy story tellers. They understand language patterns well enough to write convincing language, but they don’t understand what they’re saying at all.
The metaphorical human equivalent would be having someone write a song in a foreign language they barely understand. You can get something that sure sounds convincing, sounds good even, but to someone who actually speaks Spanish it’s nonsense.
Calculators don’t understand maths, but they are good at it.
LLMs speak many languages correctly, they don’t know the referents, they don’t understand concepts, but they know how to correctly associate them.
What they write can be wrong sometimes, but it absolutely makes sense most of the time.
I’d contest that, that shouldn’t be taken for granted. I’ve tried several questions in these things, and rarely do I find an answer entirely satisfactory (though it normally sounds convincing/is grammatically correct).
This is the reply to your message by our common friend:
I’d say it does make sense
https://youtu.be/-VsmF9m_Nt8
Song written by an Italian intended to sound like american accented english but its intentionally gibberish.
Here is an alternative Piped link(s): https://piped.video/-VsmF9m_Nt8
Piped is a privacy-respecting open-source alternative frontend to YouTube.
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GPT can write and edit code that works. It simply can’t be true that it’s solely doing language patterns with no semantic understanding.
To fix your analogy: the Spanish speaker will happily sing along. They may notice the occasional odd turn of phrase, but the song as a whole is perfectly understandable.
Edit: GPT can literally write songs that make sense. Even in Spanish. A metaphor aiming to elucidate a deficiency probably shouldn’t use an example that the system is actually quite proficient at.
Sure it can, “print hello world in C++”
#include int main() { std::cout << "hello world\n"; return 0; }“print d ft just rd go t in C++”
#include int main() { std::cout << "d ft just rd go t\n"; return 0; }The latter is a “novel program” it’s never seen before, but it’s possible because it’s seen a pattern of “print X” and the X goes over here. That doesn’t mean it understands what it just did, it’s just got millions (?) of patterns it’s been trained on.
Because it can look up code for this specific problem in its enormous training data? It doesnt need to understand the concepts behind it as long as the problem is specific enough to have been solved already.
If that were true, it shouldn’t hallucinate about anything that was in its training data. LLMs don’t work that way. There was a recent post with a nice simple description of how they work, but I’m not finding it. If you’re interested, there’s plenty of videos and articles describing how they work.
It doesn’t have the ability to just look up anything from its training data, that stuff is encoded in its parameters. Still, the input has to be encoded in a way that causes the correct “chain reaction” of excited/not excited neurons.
Beyond that, it’s not just a carbon copy from what was in the training either because you can tell it what variable names to use, which order to do things in, change some details, etc. If it was simply a lookup that wouldn’t be possible. The training made it able to generalize what it learned to some extent.
Yes, but it doesnt do so because it understands what a variable is, it does so because it has statistics as to where variables belong most likely.
In a way it is like the guy that won the french scrabble championship without speaking a single word of french, by learning the words in the dictionary.
I can tell GPT to do a specific thing in a given context and it will do so intelligently. I can then provide additional context that implicitly changes the requirements and GPT will pick up on that and make the specific changes needed.
It can do this even if I’m trying to solve a novel problem.
But the naysayers will argue that your problem is not novel and a solution can be trivially deduced from the training data. Right?
I really dislike the simplified word predictor explanation that is given for how LLM’s work. It makes it seem like the thing is a lookup table, while ignoring the nuances of what makes it work so well.
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