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AI models feeding on AI data will lead to 'model collapse', researchers say (web.archive.org)

submitted 1 year ago by 0x815@feddit.de to c/technology@beehaw.org

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Using model-generated content in training causes irreversible defects, a team of researchers says. "The tails of the original content distribution disappears," writes co-author Ross Anderson from the University of Cambridge in a blog post. "Within a few generations, text becomes garbage, as Gaussian distributions converge and may even become delta functions."

Here's is the study: http://web.archive.org/web/20230614184632/https://arxiv.org/abs/2305.17493

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[–] coolin@beehaw.org 2 points 1 year ago (2 children)

This isn't an actual problem. Can you train on post-ChatGPT internet text? No, but you can train on the pre-ChatGPT common crawls, the millions of conversations people have with the models and on audio, video and images. As we improve training techniques and model architectures, we will need even less of this data to train even more performant models.

[–] interolivary@beehaw.org 7 points 1 year ago (1 children)

But then you're training on more and more outdated data

[–] Kerb@discuss.tchncs.de 1 points 1 year ago

Afaik, there are already solution to that.

You first train the data on the outdated but correct data, to establish the correct "thought" patterns.

And then you can train the ai on the fresh but flawed data, without tripping about the mistakes.

[–] artificial_unintelligence@programming.dev 4 points 1 year ago

I think it’s not a hard stop but it is an issue. I think it will force models to be trained in more novel ways, rather than just purely pump more data in. I think ideally we’d be able to reach GPT level intelligence on fractions of the data and compute. These new techniques have yet to be made but this will put pressure on their creation