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Training on Ny Times and Wikipedia… The Wikipedia part at least, is incorrect. Currently Wikipedia mods/admins are dealing with AI generated articles being upl

Training on Ny Times and Wikipedia… The Wikipedia part at least, is incorrect. Currently Wikipedia mods/admins are dealing with AI generated articles being uploaded. As for NYT - I am assuming that lots of those stories are already available in some blog or the other. The e-mail and web forums are 100% polluted with spam, which takes constant effort to remove. For GenAI based content, it is far harder to identify and remove. This example assumes the effort required to keep the web functional can deal with AI created content. Speaking from experience, our filters (human and otherwise) cannot. They fail to do so even now. PS: Even given your example of closed email chains - the information in that depends on sources people read. Like plastic pollution in the food chain, this is inescapable.

crazygringo 2y ago
> Currently Wikipedia mods/admins are dealing with AI generated articles being uploaded. And they've always dealt with spam and low-quality submissions before. The system is working. > As for NYT - I am assuming that lots of those stories are already available in some blog or the other. I don't know what relevance that has to what we're talking about. The point is, train on the NYT. Blogs don't change what's on the NYT. > The e-mail and web forums are 100% polluted with spam, which takes constant effort to remove. They've always been polluted with low-quality content. So yes, either don't train on them, or only train on highly upvoted solutions, etc. AI pollution isn't fundamentally any different from previous low-quality content and spam. It's not terribly difficult to determine which parts of the internet are known to be high-quality and train only on those. LLM's can't spam the NY Times.
- intended 2y ago
  >The system is working. Given that this is an issue I've heard from wikipedia admins themselves, I am impressed by your confidence. >The point is, train on the NYT. Blogs don't change what's on the NYT. The counter point is that NYT content is already in the training data because its already replicated or copied into random blogs. >So yes, either don't train on them, or only train on highly upvoted solutions, etc. Highly upvoted messages on reddit are very regular bots copying older top comments. Mods already have issues with AI comments. ---- TLDR: Pollution is already happening. Verification does not scale, while generation scales.
  - crazygringo 2y ago
    > The counter point is that NYT content is already in the training data That's not a counter point. My point is, train on things like the NYT, not random blogs. You can also whitelist the blogs you know are written by people, rather than randomly spidering the whole internet. Also, no -- most of the NYT hasn't been copied into blogs. A small proportion of top articles, maybe. > Highly upvoted messages on reddit are very regular bots copying older top comments. What does that matter if the older top comment was written by a person? Also, Reddit is not somewhere you want to train in the first place if you're trying to generate a model where factual accuracy matters. > Verification does not scale, while generation scales. You don't need to verify everything -- you just need to verify enough stuff to train a model on. We're always going to have plenty of stuff that's sufficiently verified, whether from newspapers or Wikipedia or whitelisted blogs or books from verified publishers or whatever. It's not a problem. You shouldn't be training on blogspam from random untrusted domains in the first place. So it doesn't matter if that junk is AI-generated or not.
    - intended 2y ago
      >What does that matter if the older top comment was written by a person? That is the entire issue? LLMs fail when they are trained on GenAI based content? > Also, Reddit is not somewhere you want to train in the first place if you're trying to generate a model where factual accuracy matters. There is no model that can create facutal accuracy. This would basically contravene the laws of physics. LLMs predict the next token. >You shouldn't be training on blogspam from random untrusted domains in the first place. So it doesn't matter if that junk is AI-generated or not Afaik, all the current models are trained on this corpus. That is how they work.
      - crazygringo 2y ago
        > There is no model that can create facutal accuracy. Factual accuracy is not binary, it is a matter of degrees. Obviously training on content that is more factually correct will result in more factually correct next tokens. This is a pretty fundamental aspect of LLM's. > Afaik, all the current models are trained on this corpus. Then apologies for being so blunt, but you know wrong. There is a tremendous amount of work that goes on by the LLM companies in verifying, sanitizing, and structuring the training corpuses, using a wide array of techniques. The are absolutely not just throwing in blogspam and hoping for the best.

crazygringo 2y ago

> Currently Wikipedia mods/admins are dealing with AI generated articles being uploaded. And they've always dealt with spam and low-quality submissions before. The system is working. > As for NYT - I am assuming that lots of those stories are already available in some blog or the other. I don't know what relevance that has to what we're talking about. The point is, train on the NYT. Blogs don't change what's on the NYT. > The e-mail and web forums are 100% polluted with spam, which takes constant effort to remove. They've always been polluted with low-quality content. So yes, either don't train on them, or only train on highly upvoted solutions, etc. AI pollution isn't fundamentally any different from previous low-quality content and spam. It's not terribly difficult to determine which parts of the internet are known to be high-quality and train only on those. LLM's can't spam the NY Times.

intended 2y ago
>The system is working. Given that this is an issue I've heard from wikipedia admins themselves, I am impressed by your confidence. >The point is, train on the NYT. Blogs don't change what's on the NYT. The counter point is that NYT content is already in the training data because its already replicated or copied into random blogs. >So yes, either don't train on them, or only train on highly upvoted solutions, etc. Highly upvoted messages on reddit are very regular bots copying older top comments. Mods already have issues with AI comments. ---- TLDR: Pollution is already happening. Verification does not scale, while generation scales.
- crazygringo 2y ago
  > The counter point is that NYT content is already in the training data That's not a counter point. My point is, train on things like the NYT, not random blogs. You can also whitelist the blogs you know are written by people, rather than randomly spidering the whole internet. Also, no -- most of the NYT hasn't been copied into blogs. A small proportion of top articles, maybe. > Highly upvoted messages on reddit are very regular bots copying older top comments. What does that matter if the older top comment was written by a person? Also, Reddit is not somewhere you want to train in the first place if you're trying to generate a model where factual accuracy matters. > Verification does not scale, while generation scales. You don't need to verify everything -- you just need to verify enough stuff to train a model on. We're always going to have plenty of stuff that's sufficiently verified, whether from newspapers or Wikipedia or whitelisted blogs or books from verified publishers or whatever. It's not a problem. You shouldn't be training on blogspam from random untrusted domains in the first place. So it doesn't matter if that junk is AI-generated or not.
  - intended 2y ago
    >What does that matter if the older top comment was written by a person? That is the entire issue? LLMs fail when they are trained on GenAI based content? > Also, Reddit is not somewhere you want to train in the first place if you're trying to generate a model where factual accuracy matters. There is no model that can create facutal accuracy. This would basically contravene the laws of physics. LLMs predict the next token. >You shouldn't be training on blogspam from random untrusted domains in the first place. So it doesn't matter if that junk is AI-generated or not Afaik, all the current models are trained on this corpus. That is how they work.
    - crazygringo 2y ago
      > There is no model that can create facutal accuracy. Factual accuracy is not binary, it is a matter of degrees. Obviously training on content that is more factually correct will result in more factually correct next tokens. This is a pretty fundamental aspect of LLM's. > Afaik, all the current models are trained on this corpus. Then apologies for being so blunt, but you know wrong. There is a tremendous amount of work that goes on by the LLM companies in verifying, sanitizing, and structuring the training corpuses, using a wide array of techniques. The are absolutely not just throwing in blogspam and hoping for the best.