In my experience, LLMs are poor at working with unpopular languages -- probably because their training data does not contain a lot of examples of programs written in those languages, or explanations of them.
So, in other words, they perform precisely how you’d expect a stochastic parrot to perform?
The more popular the language the more likely the training corpus includes both very similar code samples and explanation of those code samples, and also the more likely those two converge on a “reasonable” explanation.
Ask it something it’s likely to have seen an answer for and it’s likely to spit out that answer… interesting? Sure, impressive? Maybe… but still pretty well captured by “a fuzzy jpeg of the web”.
> Train a human mostly on English, and they'll speak English. Train them mostly on Chinese, and they'll speak Chinese.
Ahh, but ask a human a question in a language they don’t understand and they’ll look at you with bewilderment, not confidently make up a stream of hallucinatory nonsense that only vaguely looks statistically right.
> Or exactly like you’d expect a human to perform.
Not exactly, no… but with just enough of the uncanny valley to make me think the more interesting thought: are we really not much more than stochastic parrots? Or, in other words, are we naturally just slightly more interesting than today’s state of the artificially stupid?
They do much better with popular languages.