Program Synthesis with Large Language Models

Can computers write Python from plain English? New tests reveal surprises

Can a computer turn a sentence into a working program? Researchers tried many models, from small ones to very big, and gave them two sets of problems: simple tasks for beginners and harder math-style puzzles.
Bigger models did better, but not perfect.
Without extra training the largest model solved about 59.
6% of the simple problems, and with extra examples it got roughly ten points better.
On the tougher math set the best tuned model reached 83.
8% accuracy.
People kept talking with the model and gave tips, and those hints helped a lot — in fact human feedback cut mistakes about in half.
Still, the models hit limits: they can write many programs but they often can’t say what those programs will actually do when run, so predicting exact program outputs remains hard.
This means these tools are useful but not foolproof; they can speed up work, spark ideas, and make coding easier, yet they need human checks and a careful eye, because errors hide in plain sight.

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Program Synthesis with Large Language Models

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