I’m no expert, but if my understanding of LLMs as ‘probability calculators’ is correct, then if you prompted one properly to produce code which does x (a Photoshop function for example) and it had only been trained on one example of this (the Photoshop source code) it would reproduce the PS source code because that has the highest probability of that being the correct ‘answer’.
This is baasically correct, though what the models actually produce is a little more randomized than that. Though, if there aren’t any words that are anywhere cllose to being likely to follow next, then there won’t be a any other valid options to choose.
I’m no expert, but if my understanding of LLMs as ‘probability calculators’ is correct, then if you prompted one properly to produce code which does x (a Photoshop function for example) and it had only been trained on one example of this (the Photoshop source code) it would reproduce the PS source code because that has the highest probability of that being the correct ‘answer’.
This is baasically correct, though what the models actually produce is a little more randomized than that. Though, if there aren’t any words that are anywhere cllose to being likely to follow next, then there won’t be a any other valid options to choose.