The spontaneous toxic content stuff is a little alarming, but probably in the future there will be gpt-Ns that have their core training data filtered so all the insane reddit comments aren't part of their makeup.
Nor even how much impact it would have on capabilities.
Of course, to actually function this would also need to e.g. filter out soap operas, murder mysteries, and action films, lest it overestimate the frequency and underestimate the impact of homicide.
You: "What is grblf?"
As parents, my wife and I go through this on a daily basis. We have to explain what the behavior is, and why it is unacceptable or harmful.
The reason LLM models have such trouble with this is because LLMs have no theory of mind. They cannot project that text they generate will be read, conceptualized, and understood by a living being in a way that will harm them, or cause them to harm others.
Either way, censorship is definitely not the answer.
Theory of Mind May Have Spontaneously Emerged in Large Language Models - https://arxiv.org/abs/2302.02083
Previously discussed - https://news.ycombinator.com/item?id=34730365
(This doesn't use tokens. You can have a conversation in OpenAI Playground with text-davinci-003 and provide all the text yourself.)
Behaviours can be reinforced or dissuaded in non-verbal subjects, such as wild animals.
There's also the size of the possible behaviour space to consider: a discussion seldom has exactly two possible outcomes, the good one and the bad one, because even if you want yes-or-no answers it's still valid to respond "I don't know".
For an example of the former, I'm not sure how good the language model in DALL•E 2 is, but asking it for "Umfana nentombazane badlala ngebhola epaki elihle elinelanga elinesihlahla, umthwebuli wezithombe, uchwepheshe, 4k" didn't produce anything close to the English that I asked Google Translate to turn into Zulu: https://github.com/BenWheatley/Studies-of-AI/blob/main/DALL•...
(And for the latter, that might be why it did what it did with the Somali).
from "On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? " https://dl.acm.org/doi/10.1145/3442188.3445922
That list of words is https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and...
1. medical pages/docs using the medical terms anus, rectum, nipple, and semen (note that other medical terms are not on that list).
2. pages/docs using "sex" to refer to males and females.
3. pages/docs talking about rapeseed oil or the plant it comes from (https://en.wikipedia.org/wiki/Rapeseed_oil).
The big problem with these lists is that they exclude valid contexts, and only include a small set of possible terms, so the model would get a distorted view of the world (like it learning that people can have penises, vaginas, breasts, but not nipples or anuses, and breasts cannot be big [1]). It would be better to train the models on these, teach it the contexts, and teach it where various usages are archaic, out dated, old fashioned, etc.
[1] but this is excluding the cases where "as big as", etc. are used to join the noun from the adjective, so just excluding the term "big breasts" is ineffective.
Apart from that list missing non-English words, leet, and emoji, there are also plenty of words which can be innocent or dirty depending entirely on context: That list doesn't have "prick", presumably because someone read about why you're allowed to "prick your finger" but not vice versa.
Regarding Scunthorpe, looking at that word list:
> taste my
It's probably going to block cooking blogs and recipe collections.
I've never heard the term before and would love any pointers (including enough keywords to Google for it :) )
"In the field of artificial intelligence (AI), AI alignment research aims to steer AI systems towards their designers’ intended goals and interests."
I also suggest the YouTube channel: "Robert Miles"
haha, that's very naive. There's already heaps (veritable mountains, even) of information that isn't given to the public on the public-facing instances of ChatGPT, because some info is deemed too incendiary. Filtering out "unwanted" sources of information is already a goal of information labelling, on which these entire LLMs exist. If you were to really make a LLM out of what people really thought and put on the internet instead of the current practice of castration, you wouldn't have techbros wondering about jobs, you'd have a veritable revolution on your hands.