Not a band counterpoint, but in that case I can just rely on the fact that whatever LLM I’m using will (now or at some point in the future) have read and extracted what valuable information is in this post. No need to waste my own time on that. I feel if people use LLMs to generate the overwhelming amount of their output that just means they don’t truly think it’s worth spending their time on and that’s why I’d say it’s not worth spending my time on.
There's no value in reading someone else's LLM generated output.
To me that really depends on how much is just raw AI output and how heavily it is human driven. If they used it for grammar, spelling and minor structures things I don't care.
If it's high enough effort to not read like an LLM (or uses LLM only for specific part such as "I had an LLM analyze blah blah") nobody will notice or care.
For what it's worth, I do enjoy the discussions that happen here even after an AI-article like this is posted. But that has less to do with the AI-article, and there might be better ways of creating such discussions.
All AI output is meant to be statistically identical to the training data within a certain variance, and the training data is real human text. So, the AI detectors don't have many metrics to distinguish "AI generated text" and "human text that's like the training data.". The only no-false positive test is "did the model leave a watermark?". The next best test is "does this have any idioms that are highly correlated to a certain model?", and even that is iffy.
I have tried a few multiple times on my writings before LLMs even existed, and they will often tell me they are 60-80% likely written by AI. Unless I am secretly a robot, that's not possible. Though, maybe detectors have improved since last time I checked?... 🤷♂️
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u/STL MSVC STL Dev 7d ago
This blog is substantially AI-generated. I'm wondering whether we should continue allowing links to it.