This article is great. And the blog-article headline is interesting, but wrong. LLM's don't in general write plausible code (as a rule) either.
They just write code that is (semantically) similar to code (clusters) seen in its training data, and which haven't been fenced off by RLHF / RLVR.
This isn't that hard to remember, and is a correct enough simplification of what generative LLMs actually do, without resorting to simplistic or incorrect metaphors.
They just write code that is (semantically) similar to code (clusters) seen in its training data, and which haven't been fenced off by RLHF / RLVR.
This isn't that hard to remember, and is a correct enough simplification of what generative LLMs actually do, without resorting to simplistic or incorrect metaphors.