It appears that GPT-4 excels at resolving coding challenges more effectively in Swift compared to Python. Could this disparity be attributed to Swift’s more rigorous syntax requirements? An intriguing thought.
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This is a very interesting idea.
I’ve been operating under the assumption that the model is likely best at Python because that’s what it’s seen the most of, but the idea that much more rigid languages—being more uniform in their examples—will be much easier for the model to accurately predict makes a great deal of sense.
ETA: I’ve been thinking a lot lately about the paper Textbooks Are All You Need, and wondering about how the quality of the model would change if there was some extensive pre-processing of the raw training data.
For instance, a spelling, grammar, and formatting sweep, might improve responses slightly.
More relevant to this topic though is the idea of what would happen if every snippet of code were passed through a linter to enforce best practices and a consistent style guide?
Might we see much stronger connections made? Would such a model more consistently generate functional, high-quality code?
I don’t know.
But, Python makes up about 17% of the code on GitHub, Swift about 1%. If what you’re seeing is accurate it’s reasonable to think enforcing uniformity within languages would improve the performance of generations within that language.
I suspect no one can really say for sure right now and it would be a huge undertaking to first generate the synthetic data and then again to actually train a new model.
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