[zekica]

You can't think all the way about refining your prompt for LLMs as they are probabilistic. Your re-prompts are just retrying until you hit a jackpot - refining only works to increase the chance to get what you want.

When making them deterministic (setting the temperature to 0), LLMs (even new ones) get stuck in loops for longer streams of output tokens. The only way to make sure you get the same output twice is to use the same temperature and the same seed for the RNG used, and most frontier models don't have a way for you to set the RNG seed.

[red75prime]

Randomness is not a problem by itself. Algorithms in BQP are probabilistic too. Different prompts might have different probabilities of successful generation, so refinement could be possible even for stochastic generation.

And provably correct one-shot program synthesis based on an unrestricted natural language prompt is obviously an oxymoron. So, it's not like we are clearly missing the target here.

[fauigerzigerk]

>Different prompts might have different probabilities of successful generation, so refinement could be possible even for stochastic generation.

Yes, but that requires a formal specification of what counts as "success".

In my view, LLM based programming has to become more structured. There has to be a clear distinction between the human written specification and the LLM generated code.

If LLMs are a high level programming language, it has to be clear what the source code is and what the object code is.

[fulafel]

Programs written in traditional PLs are also often probabilistic. It seems that the same mechanisms could be used to address this in both types (formal methods).

[bandrami]

Huh?

What's an example of a probabilistic programming language?

[cubefox]

... yet.