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this post was submitted on 02 Aug 2023
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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.
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.
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.
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.