I was thinking about this after a discussion at work about large language models (LLMs) - the initial scrape of the internet before Chat GPT become publicly usable was probably the last truly high quality scrape of human-made content any model will get. The second Chat GPT went public, the data pool became tainted with people publishing information from it. Future language models will have increasingly large percentages of their data tainted by AI-generated content, skewing the results away from how humans actually write. To get actual human content, they may need to turn to transcriptions of audio recordings or phone calls for training, and even that wouldn’t be quite correct because people write differently than they speak.
I sort of wonder if eventually people will start being influenced in how they choose to write based on seeing this AI content. If teachers use AI-generated texts in school lessons, especially at lower levels, will that effect how kids end up writing and formatting their work? It’s weird to think about the wider implications of how this AI stuff will ultimately impact society.
What’s your predictions? Is there a future where AI can get a clean, human-made scrape? Are we doomed to start writing like AIs?
It’s not going to replace actual dedicated writers, but it’s definitely going to hinder people learning to write and make up a large portion of the text online. It may also make it harder for actual writers to be found in all the noise. I heard a little while back about a scifi magazine which had to close its submissions because it was getting too many AI-written stories and sorting through the real versus fake was becoming difficult for them.
As for who’s going to train the AI, that’s part of what I’m arguing here - future LLMs are going to wind up being trained on AI-generated text because there will be so much of it online that screening it out becomes near impossible. Reddit mods already have challenges screening out chat GPT bots from their comments. When a future LLM scrapes the web for writen words, it’ll come back with lots of garbage AI text which will taint its learning pool. AIs will learn from AIs and become worse for it.
This is a classic feedback problem: you use a microphone to amplify your voice, but If the mic picks up the amplified sound it creates audio feedback + a sharply increasing wail.
I can’t imagine what LLM feed back ‘sounds like’, but a guarantee you it ain’t pretty.
I think it could end up being a problem that we face in the future, but probably not an insurmountable one.
For one, I suspect that clean data sources will always be available, though it could become a lot more expensive to obtain. As an extreme example, you could always source your data by recording in-person conversations.
Also, as AI improves, I’m guessing it will be able to handle bad data more gracefully, and that it should be able to train to the same effectiveness while using a smaller dataset.
I feel like if you tried to train an LLM on spoken conversational English the output would just be “yeah um yeah um yeah um”
But on a more serious note spoken English is very different than written.
Either way you can find validated sources of human written text it just won’t be as easy.
You have to remember one thing, writing or speaking of a language is not a fixed scientific law or math formula that will stay true through out history. A living language is always moving and evolving in most of its components, be a vocabulary, grammar, or even meaning of words/phrases. We are just entering an era where AI generated content someone might feel appealing and follow that style, compare to copy a contemporary popular writer.
Indeed. As long as the language is still expressive and we understand what is being communicated, I don’t see why it would matter if it “sounds like” AI or not.
If it really becomes a problem then just curate the training data better to exclude the stuff that “sounds like” an AI. Doesn’t matter if it’s actually written by an AI or not, just select the training data that matches what you’d like the AI to learn and go with that. There’s not some kind of magical ghost present in human-written words that’s absent in AI-written words, if the words are the words you want then that’s all that matters.
I suspect the quality LLM development teams will pursue the same in-depth data sourcing & cleaning techniques that quality ML researchers are developing today. Or rather, they’ll do something similar in principle to mitigate this issue.
I still agree with your conclusions. It will be a bigger consideration and less scrupulous teams will be more effected.
