for ML engineers: why can't you simply exclude the word "fuck"?
So, I’ve heard that ML manipulates tokens and specifically for the English corpora they take place of words. If we want model to be polite and not to speak uncomfortable language we can remove certain words from the internal array where all tokens and their associative data are stored, for example “fuck”.
Why? There are so many snowflakes around, that you’d get texts like this:
**’ ***** **** ***** ****!
I’d say get a life and don’t be offended so much. On the other hand, the human plague would be solved fast when nobody would fuck anymore. You’re on to something. ;)
As others have mentioned, it’s not quite that simple.
For starters, you can absolutely remove the word “fuck” from all the training data. Now it’s literally impossible for the AI to “know” the word. But what do you do with the training data? Do you replace “fuck” with a different token? “****” perhaps? Or do you just drop the data entirely?
Giving “offense” is much more complex than just a single word. See, if we just replace the token, the AI may still decide that “Go **** yourself” is a perfectly valid response to a query. On the other hand, if you drop all instances of "fuck"from the data, your AI will just learn offensive euphemisms instead: “You can shove your request where the sun don’t shine”
Worse, there are plenty of sexual / offensive phrases that are built up from perfectly innocuous tokens. “Prone bone”, for instance.
The goal with these (and really almost all) AI models is for them to be “helpful, honest, and harmless”. Simply alerting or replacing a single token (or even combination of tokens) doesn’t really help, because the AI is modeling concepts, not just individual words.
All of this to say that the problem being solved is not to stop an AI from saying “fuck” - it’s to build an AI that doesn’t want to.
ML/Generative AI don’t “store” an internal array of specifics. Instead it’s a statistical model based on the next (or in ChatGPT’s case, 3rd most likely) word in a sentence.
To avoid swearing or other really anything it needs to be excluded at a training level, before the algorithm is trained.
As it stands, we have very little to no visibility into why these models work. Even the researchers are trying to open the black box, but there’s so much that it’s nearly impossible to isolate a node that would or would not contain the work fuck
I believe that the 3rd or nth, word is because it sounds more human. The statistically first correct word ends up sounding very robotic and forced, where the 3rd is still very likely correct, but leads to variation in responses
This is all from what I remember reading a mini-paper about it, so I could be wrong
Chatgpt’s sampling parameters are unknown, and it definitely doesn’t choose the 3rd most likely. More complicated sampling methods are probably used, such as temperature, top p and top k.
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