Completely the same as regular text, except punctuations are centered instead of staying at the bottom of the line. CJK Han likely encodes tokens to character one on one. Which brings an interesting question, are Chinese characters more efficient for NLP? In the sense that semantic meaning of a word is not chopped up into partial "tokens".
Perhaps relevant: Chinese language is not more efficient than English in vibe coding: A preliminary study on token cost and problem-solving rate <a href="https://arxiv.org/abs/2604.14210v1" rel="nofollow">https://arxiv.org/abs/2604.14210v1
This is interesting, thank you. It also reinforces the stocastic parrot theory of LLM. Maybe one mythical day when the majority of codebases around the world are written in CJK Han, vibe coding will become more efficient in Chinese
How does it say anything about the stochastic parrot theory of LLMs?
The only thing there that seems like it's relevant is the finding that the models they used had worse results when prompted in Chinese than when prompted in English. But ...
I am fairly sure I would do substantially worse work if I had to do it in French rather than in English, even if the work itself was all mathematics and programming and the like. Maybe that in some sense indicates that I am a stochastic parrot but it clearly doesn't indicate that I'm a stochastic parrot in some way worse than human beings are since I happen to be a human being myself.
... so what am I missing, that makes the models' worse performance in Chinese an indication that they're stochastic parrots in any interesting sense?
(I take it that "they're stochastic parrots just like we are" is not a very interesting sense. I mean, actually it would be quite interesting to understand better how much of human thinking can be reasonably described as stochastic-parroting -- fairly clearly the amount isn't zero -- but I think it would be interesting as a finding in human psychology, not as a fact about LLMs.)
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.)
kittikitti · · focus · HN ↗
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altairprime · · focus · HN ↗
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gjm11 · · focus · HN ↗
The only thing there that seems like it's relevant is the finding that the models they used had worse results when prompted in Chinese than when prompted in English. But ...
I am fairly sure I would do substantially worse work if I had to do it in French rather than in English, even if the work itself was all mathematics and programming and the like. Maybe that in some sense indicates that I am a stochastic parrot but it clearly doesn't indicate that I'm a stochastic parrot in some way worse than human beings are since I happen to be a human being myself.
... so what am I missing, that makes the models' worse performance in Chinese an indication that they're stochastic parrots in any interesting sense?
(I take it that "they're stochastic parrots just like we are" is not a very interesting sense. I mean, actually it would be quite interesting to understand better how much of human thinking can be reasonably described as stochastic-parroting -- fairly clearly the amount isn't zero -- but I think it would be interesting as a finding in human psychology, not as a fact about LLMs.)
fyredge · · focus · HN ↗
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.
gjm11 · · focus · HN ↗
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.)