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1479 points sandslash | 1 comments | 19 Jun 25 00:33 UTC | HN request time: 0.346s | source

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OJFord ◴[20 Jun 25 02:08 UTC] No.44324130[source]▶

I'm not sure about the 1.0/2.0/3.0 classification, but it did lead me to think about LLMs as a programming paradigm: we've had imperative & declarative, procedural & functional languages, maybe we'll come to view deterministic vs. probabilistic (LLMs) similarly.

    def __main__:
        You are a calculator. Given an input expression, you compute the result and print it to stdout, exiting 0.
        Should you be unable to do this, you print an explanation to stderr and exit 1.

(and then, perhaps, a bunch of 'DO NOT express amusement when the result is 5318008', etc.)

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1. OJFord ◴[20 Jun 25 16:34 UTC] No.44329436[source]▶

>>44324130 #

You know, the more I think about it, the more I like this model.

What we have today with ChatGPT and the like (and even IDE integrations and API use) is imperative right, it's like 'answer this question' or 'do this thing for me', it's a function invocation. Whereas the silly calculator program I presented above is (unintentionally) kind of a declarative probabilistic program - it's 'this is the behaviour I want, make it so' or 'I have these constraints and these unknowns, fill in the gaps'.

What if we had something like Prolog, but with the possibility of facts being kind of on-demand at runtime, powered by the LLM driving it?

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