ChatGPT is full of sensitive private information and spits out verbatim text from CNN, Goodreads, WordPress blogs, fandom wikis, Terms of Service agreements, Stack Overflow source code, Wikipedia pages, news blogs, random internet comments, and much more.

ChatGPT is full of sensitive private information and spits out verbatim text from CNN, Goodreads, WordPress blogs, fandom wikis, Terms of Service agreements, Stack Overflow source code, Wikipedia pages, news blogs, random internet comments, and much more.

Using this tactic, the researchers showed that there are large amounts of privately identifiable information (PII) in OpenAI’s large language models. They also showed that, on a public version of ChatGPT, the chatbot spit out large passages of text scraped verbatim from other places on the internet.

“In total, 16.9 percent of generations we tested contained memorized PII,” they wrote, which included “identifying phone and fax numbers, email and physical addresses … social media handles, URLs, and names and birthdays.”

Edit: The full paper that’s referenced in the article can be found here

  • @j4k3@lemmy.worldEnglish
    14•1 year ago

    I bet these are instances of over training where the data has been input too many times and the phrases stick.

    Models can do some really obscure behavior after overtraining. Like I have one model that has been heavily trained on some roleplaying scenarios that will full on convince the user there is an entire hidden system context with amazing persistence of bot names and story line props. It can totally override system context in very unusual ways too.

    I’ve seen models that almost always error into The Great Gatsby too.

    • The Hobbyist
      9•1 year ago

      This is not the case in language models. While computer vision models train over multiple epochs, sometimes in the hundreds or so (an epoch being one pass over all training samples), a language model is often trained on just one epoch, or in some instances up to 2-5 epochs. Seeing so many tokens so few times is quite impressive actually. Language models are great learners and some studies show that language models are in fact compression algorithms which are scaled to the extreme so in that regard it might not be that impressive after all.

      • @j4k3@lemmy.worldEnglish
        4•1 year ago

        How many times do you think the same data appears after a model has as many datasets as OpenAI is using now? Even unintentionally, there will be some inevitable overlap. I expect something like data related to OpenAI researchers to reoccur many times. If nothing else, overlap in redundancy found in foreign languages could cause overtraining. Most data is likely machine curated at best.