List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words

Good point. I am already checking against the naughty-words list from here: https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and...

"The Colossal Clean Crawled Corpus, used to train a trillion parameter LM in [43], is cleaned, inter alia, by discarding any page containing one of a list of about 400 “Dirty, Naughty, Obscene or Otherwise Bad Words”. This list is overwhelmingly words related to sex, with a handful of racial slurs and words related to white supremacy (e.g. swastika, white power) included. While possibly effective at removing documents containing pornography (and the associated problematic stereotypes encoded in the language of such sites) and certain kinds of hate speech, this approach will also undoubtedly attenuate, by suppressing such words as twink, the influence of online spaces built by and for LGBTQ people. If we filter out the discourse of marginalized populations, we fail to provide training data that reclaims slurs and otherwise describes margin

You many want to consider a naughty words filter. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and...

There are some popular bad words lists on GitHub that you can use. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and...

The good news is that things like https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and... exist so getting a source of words to filter is easy enough. And converting numbers to letters isn't too bad. The hardest problem with the implementation was that with a long list you can't just search for a few dozen inappropriate words (like the Twitch implementation). It would be very expensive to do hundreds or even thousands of checks against every inappropriate word. The solution we came to was to truncate all the inappropriate words to either 3 or 4 letters and store them in a big set. We then take our generated strings, which are usually 11 characters, and break them up into all possible substrings of lengths 3 and 4. For example, 1a2b3c4d5e6 would be broken down into 1a2 a2b 2b3 b3c 3c4 c4d 4d5 5e6 1a2b a2b3 2b3c b3c4 3c4d c4d5 4d5e

I believe this is the word list that authors are objecting to the use of: https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and... The list does seem a bit... umm... oddly specific in places, probably due to its history as being first compiled for a photo sharing site.

https://www.technologyreview.com/2020/12/04/1013294/google-a... is a summary of the paper that started it, full of actionable points – and co-authored with Emily Bender, no less! Also, if you're into the LessWrong mindset and are susceptible to think that that which can be destroyed by the truth should be, you might find this tidbit interesting: > Buried in the recent trillion parameter language model paper is how the dataset to train it was created. Any page that contained one of these words was excluded: https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and... Two sample banned words: "twink" and "sex" https://twitter.com/willie_agnew/status/1350551463718621184

Almost related: https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and...

Ah wow, thanks! Not sure how I missed that. For other interested parties, here's the key section: > Unfortunately, the majority of [the text in Common Crawl] is not natural language. Instead, it largely comprises gibberish or boiler-plate text like menus, error messages, or duplicate text. Furthermore, a good deal of the scraped text contains content that is unlikely to be helpful for any of the tasks we consider (offensive language, placeholder text, source code, etc.). To address these issues, we used the following heuristics for cleaning up Common Crawl’s web extracted text: •We only retained lines that ended in a terminal punctuation mark (i.e. a period, exclamation mark, question mark, or end quotation mark). •We removed any page that contained any word on the “List of Dirty, Naughty, Obscene or Otherwise Bad Words”. [https://github.

This is where I always chime in with a link to the List of Dirty, Nasty, or Otherwise Obscene Words on github. https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and... I originally found this list during a few frantic hours after I first created a password cracking homework for my security class. Naturally I'd used the Linux dictionary in /usr/dict/words as the source of the passwords. It only occurred to me much later to check what passwords my script had randomly chosen, and by then the hashes had been distributed to the students. Whoops!

I removed it: https://github.com/shutterstock/List-of-Dirty-Naughty-Obscen...

This is the most beautiful commit message I've read in a long time. https://github.com/shutterstock/List-of-Dirty-Naughty-Obscen... Now what exactly is the thing it refers to?

This reminds me of Shutterstock's open-source List of Dirty Naughty Obscene and Otherwise Bad Words: https://github.com/shutterstock/List-of-Dirty-Naughty-Obscen...

Did they even have any sort of filter? If they at least blacklisted these words [0], then that seems like a reasonable enough effort on its own. However, these developers would have had to be living in a bubble to not know about trolls from 4chan. All in all, this is a lesson that some high-profile person/group eventually had to learn on our behalf. Now, when an unknowing manager asks why your chat bot needs to avoid certain offensive phrases because, "our clientele aren't a bunch of racists", you can just point him to this story. The actual racists are tame by comparison to what trolls will do to your software. [0] = https://github.com/shutterstock/List-of-Dirty-Naughty-Obscen...