Hashids.java VS List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words

You basically want Hashids but sequential? Why not simple convert a base 10 (0-9) number to hex? (0-F)

I found hashids [1] to be a great compromise between integer ids in the database and copyable non-enumerable strings on the client.

[1] https://hashids.org/

I wonder how this might compare to just storing regular autoincrementing ints in the database, and converting to/from hashids (https://hashids.org/) at the edge. It eliminates the collision concern and stores more compactly at the cost of a tiny amount of encode/decode when processing requests. You’d want to push it down as close to the database layer as possible to avoid inadvertent int ID leaks; I added native hashids support to clickhouse but I’m not sure what other database support might entail.

Since hashids are not really hashes and are not secure at all this is not even achieved. Hashids can be easily decoded without the salt by a simple brute-force attack described by the authors of hashid themselves right on their website: https://hashids.org/

You don't necessarily need to use UUIDs. You could use something like Hashids to generate random strings from your sequential IDs in a reversible way, so that users can't predict what their values will be, but you can decode them as needed.

You should not use UUIDv4 as a primary key. You can use normal int values and then use hashids to make them safe for URL. UUIDv7 might be good to use as well once they are more widely supported as well.

Good point. I am already checking against the naughty-words list from here:

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

https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words is one

We know that C4 was used to train Google’s influential T5 model, Facebook’s LLaMA, as well as the open source model Red Pajama. C4 is a very cleaned-up version of a scrape of the internet from the non-profit CommonCrawl taken in 2019. OpenAI’s model GPT-3 used a training dataset that began with 41 scrapes of the web from CommonCrawl from 2016 to 2019 so I think it’s safe to say that something akin to C4 was part of GPT-3. (The researchers who originally looked into C4 argue that these issues are common to all web-scraped datasets.) When we reached out to OpenAI and Google for comment, both companies emphasized that they undergo extensive efforts to weed out potentially problematic data from their training sets. But within the industry, C4 is known as being a heavily filtered dataset and has been criticized, in fact, for eliminating content related to LGBTQ+ identities because of its reliance on a heavy-handed blocklist. (https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words ) We are working on some reporting to try to address your last and very crucial question, but it’s an open area of research and one that even AI developers are struggling to answer.

"The Colossal Clean Crawled Corpus, used to train a trillion parameter LM in , 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 marginalized identities in a positive light"

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...

Yeah, This is shutterstocks one which they shared

I always turn off the chatfilter, so defo let them choose if they want to have it censored or not. For the actual words themselves, there are plenty of lists out there that you can use (like this one). Although these are just regular words, none of the circumvention methods are included

That it's very crudely filtered for naughty words. According to the paper, "We removed any page that contained any word on the “List of Dirty, Naughty, Obscene or Otherwise Bad Words”." That list is here. While it contains a lot of unquestionably ugly words, it also contains words like "tit".

No problem! I wrote a short json file and Swift script to remove the nsfw words from the prompt during the image generation process, therefore it's not based on the negative prompt. The json file is a txt full with nsfw words so the app can check and remove unwanted prompts, e.g.: https://github.com/LDNOOBW/List-of-Dirty-Naughty-Obscene-and-Otherwise-Bad-Words

This is the closest I’ve come to finding one. It’s not that great.