I am using stochastic parrot in the sense that since models are mainly trained on codebases of Latin characters, their coding strength reflects on the "goodness" of the codebases that were fed to them. More Latin codebases, better English coding performance. I am assuming from the paper, that when prompting in Chinese, the generated code is in Chinese for as much as possible (imported libs etc.).
Even if we didn't know french, it is straightforward to use deterministic tools (I.e. a dictionary) to parse the instruction, writing out the code in English, then refactor as many words as possible to french. The paper did not give any indication that this was happening (would be a novel result indeed) and so I would have to conclude that it is running like a stochastic parrot, more English codebase, better performance in English only.
I think this might still be in the realm of "maybe about as stochastic-parrot-y as human beings are"; I can easily imagine doing a worse job of remembering relevant stuff that happened to be in English if I had to do my work in some other language. Human memory is surprisingly context-dependent. But it would be interesting to see what happens if you ask an LLM to write code while talking to it in some language that has waaaaay less programming-related stuff on the internet. Swahili, perhaps. Is it much worse than when you prompt it in English, or a little worse, or what?
If the LLMs are very stochastic-parrot-y -- just piecing together bits of code associated with the words you wrote in English-or-Chinese-or-Swahili -- then I would expect them to get catastrophically worse when prompted in a language in which there's very little programming content on the internet. (For what it's worth, I think I also think this degree of stochastic-parrot-ness seems rather incompatible with what they are able to do. But others may disagree.) On the other hand, if they're more like humans -- somewhat better at remembering relevant things when they're in the same language as they're working in, etc., but operating at a conceptual level as well as pushing words around -- then I would expect the loss to be much more moderate.
(It seems somewhat relevant that the insides of a transformer network operate on embedding vectors rather than literal tokens; presumably those embedding vectors are much less language-specific.)
Even if we didn't know french, it is straightforward to use deterministic tools (I.e. a dictionary) to parse the instruction, writing out the code in English, then refactor as many words as possible to french. The paper did not give any indication that this was happening (would be a novel result indeed) and so I would have to conclude that it is running like a stochastic parrot, more English codebase, better performance in English only.