There’s no denying it. This task is intellectual. Does not involve rote memorization. There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from.
The llm understands what it is unminifying and it is in general superior to humans on this regard. But only in this specific subject.
> There are not tons and tons of data pairs on the web of minimized code and unminified code for llms to learn from.
Are you sure about this? These can be easily generated from existing JS to use as a training set, not to mention the enormous amount of non-minified JS which is already used to train it.
The challenge of understanding minified code for a human comes from opaque variable names, awkward loops, minimal whitespacing, etc. These aren't things that a computer has trouble with: it's why we minify in the first place. Attention, as a scheme, should do great with it.
I'd also say there is tons of minified/non-minified code out there. That's the goal of a map file. Given that OpenAI has specifically invested in web browsing and software development, I wouldn't be surprised if part of their training involved minified/unminified data.
They are irrelevant for executing the code, but they're probably pretty relevant for an LLM that is ingesting the code and text and inferring its function based on other examples it has seen. It's definitely more impressive that an LLM can succeed at this without the context of (correct) variable names than with them.
There was a time when winning in Chess was a proof of humans' superior intellect, and then it became just an algorithm. Then Go.
Having a computer multiplying 1000-digit numbers instantly is an example of humans succeeding at multiplying: by creating a tool for that first. Because what else is intellectually succeeding there? It’s not like the computer has created itself.
If one draws a boundary of human intelligence at the skull bone and does not count the tools that this very intelligence is creating and using as mere steps of problem solving process, then one will also have to accept that humans are not intelligent enough to fly into space or do surgery or even cook most of the meals.
GPT-4 has consumed more code than your entire lineage ever will and understands the inherent patterns between code and minified versions. Recognizing the abstract shape of code sans variable names and mapping in some human readable variable names from a similar pattern you've consumed from the vast internet doesn't seem farfetched.
LLMs are very good at a surprisingly broad array of semi-structured-text-to-semi-structured-text transformations, particularly within the manifold of text that is widely available on the internet.
It just so happens that lots of code is widely available on the internet, so LLMs tend to outperform on coding tasks. There’s also lots of marketing copy, general “encyclopedic” knowledge, news, human commentary, and entertainment artifacts (scripts, lyrics, etc). LLMs traverse those spaces handily as well. The capabilities of AI ultimately boil down to their underlying dataset and its quality.
You people are so weird.
I'm simply saying the AI has superior performance to humans on this specific subject. That's all.
Why did you suddenly make this comment of "bowing before your god" when I didn't even mention anything remotely close to that?
I'll tell you why. Because this didn't come from me. It came from YOU. This is what YOU fear most. This is what YOU think about. And your fear of this is what blinds you to the truth.
So is too multiplying _many_ large numbers together.
> The llm understands what it is unminifying and it is in general superior to humans on this regard.
Proof needed. I would grant in terms of speed as very likely to be true. In general, I do not know how accuracy would compare.