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Oops! We Automated Bullshit. | Department of Computer Science and Technology
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this post was submitted on 19 Nov 2023
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Ask it to write code that replaces every occurrence of "me" in every file name in a folder with "us", but excluding occurrences that are part of a word (like medium should not be usdium) and it will give you code that does exactly that.
You can ask it to write code that does a heat simulation in a plate of aluminum given one side of heated and the other cooled. It will get there with some help. It works. That's absolutely fucking crazy.
Maybe, that really depends on if that task or a very similar task exists in sufficient amounts in its training set. Basically, you could get essentially the same result by searching online for code examples, the LLM might just make it a little faster (and probably introduce some errors as well).
An LLM can only generate text that exists in its training data. That's a pretty important limitation, which has all kinds of copyright-related issues associated with it (e.g. I can't just copy a code example from GitHub in most cases).
No, it does not depend on preexisting tasks, which is why I told you those 2 random examples. You can come up with new, never before seen questions if you want to. How to stack a cable, car battery, beer bottle, welding machine, tea pot to get the highest tower. Whatever. It is not always right, but also much more capable than you think.
It is dependent on preexisting tasks, you're just describing encoded latent space.
It's not explicit but it's implicitly encoded.
And you still can't trust it because the encoding is intrinsically lossy.
It can come up with new solutions.
Ask it to finish writing the code to fetch a permission and it will make a request with a non-existent code. Ask it to implement an SNS API invocation and it'll make up calls that don't exist.
Regurgitating code that someone else wrote for an aluminum simulation isn't the flex you think it is: that's just an untrustworthy search engine, not a thinking machine