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by clauderoux 949 days ago
Recently I had to share a documentation written in Word, which I had to format in Markdown to put it on Github. I transformed my document into a text file for each chapter and then I asked chatGPT to transform each of these files into a Markdown page. And I also asked it to improve the English. Then I asked chatGPT to translate each of these files into different languages. The result is here: https://github.com/naver/tamgu/tree/master/documentations Basically, I did in a couple of hours, something that would have taken weeks of tedious work
1 comments
calf 949 days ago
OK then that's tedious work, not complex work.

I know that lots of people have personal stories of using ChatGPT but I was hoping something publicly reported on or like a showcase of truly impressive usages somewhere.

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clauderoux 949 days ago
You are kidding right. Taking a raw text file and detecting every single header, sub-headers, keywords and pieces of code to add the right markdown tags is a simple task to you?

Have you ever tried to make a Python program to do exactly that?

I only used couple of sentences to build my prompt...

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calf 949 days ago
No, what is the computational complexity of the task? The computational complexity of the problem is not about how long your code is or how long you took to write it.

I don't know your CS background but perhaps I do not view the terms "complex" and "tedious" the way you assume. A tedious parser is certainly tedious to write, but it is not (necessarily) complex. And from an engineering standpoint it is questionable that you lost all the formatting information from Word, which would have already demarcated what things were headers, code, and so forth. So, you had to use a roundabout way—an LLM—to recover that information from the semantics.

If what you're really arguing is that ChatGPT works well for language translation tesks, in this case translating mixed prose, code, and foreign languages--sure I guess that's great at productivity and removing tedium, but it's not that surprising a usage given what LLMs are. They are language translators.

In other words you're saying it's complex but your argument reduces a task that is straightforward but tedious for humans, to the problem complexity of natural language processing.

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