Researchers developed a more efficient way to control the outputs of a large language model, guiding it to generate text that adheres to a certain structure, like a programming language, and remains error free.
Wasn’t “error-free” one of the undecidable problems in maths / computer science? But I like how they also pay attention to semantics and didn’t choose a clickbaity title. Maybe I should read the paper, see how they did it and whether it’s more than an AI agent at the same intelligence level guessing whether it’s correct. I mean surprisingly enough, the current AI models usually do a good job generating syntactically correct code one-shot. My issues with AI coding usually start to arise once it gets a bit more complex. Then it often feels like poking at things and copy-pasting various stuff from StackOverflow without really knowing why it doesn’t deal with the real-world data or fails entirely.
Wasn’t “error-free” one of the undecidable problems in maths / computer science? But I like how they also pay attention to semantics and didn’t choose a clickbaity title. Maybe I should read the paper, see how they did it and whether it’s more than an AI agent at the same intelligence level guessing whether it’s correct. I mean surprisingly enough, the current AI models usually do a good job generating syntactically correct code one-shot. My issues with AI coding usually start to arise once it gets a bit more complex. Then it often feels like poking at things and copy-pasting various stuff from StackOverflow without really knowing why it doesn’t deal with the real-world data or fails entirely.