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>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? > Al

>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.
- intended 2y ago
  Thank you for being blunt. Let me attempt to speak in the same earnest tone. You are contradicting the papers and work that the people who make the models are saying. Alternatively, you are looking at the dataset curation process with rose tinted glasses. >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. Common crawl is instrumental in building our models, 60% of GPT's training data was Common Crawl. (https://arxiv.org/pdf/2005.14165 https://arxiv.org/pdf/2005.14165) pg 9. CC in turn was never intended for LLM training, this misalignment in goals results in downstream issues like hate speech, NYT content, copyrighted content and more getting used to train models. https://foundation.mozilla.org/en/research/library/generative-ai-training-data/common-crawl/ https://foundation.mozilla.org/en/research/library/generativ... (This article is to establish the issues with CC as a source of LLM training) https://facctconference.org/static/papers24/facct24-148.pdf https://facctconference.org/static/papers24/facct24-148.pdf (this details those issues.) Firms, such as the NYT are now stopping common crawl from archiving their pages. https://www.wired.com/story/the-fight-against-ai-comes-to-a-foundational-data-set/ https://www.wired.com/story/the-fight-against-ai-comes-to-a-... ----- TLDR: 'NYT' and other high quality content has largely been ingested by models. Reddit and other sources play a large part in training current models. While I appreciate your being blunt, this also means not being sharp and incisive. Perhaps precision would be required here to clarify your point. Finally - >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 What. Come on, I think you wouldnt agree with your own statement after reading it once more -Factual correctness is not a matter of degrees. Furthermore, facts dont automatically create facts. Calcuation, processing, testing and verification create more facts. Just putting facts together creates content.
  - crazygringo 2y ago
    Re: corpus content, I think we're talking past each other. I'm saying that current models aren't being blindly trained on untrusted blogspam, and that there's a lot of work done to verify, structure, transform, etc. And earlier models were trained with lower-quality content, as companies were trying to figure out how much scale mattered. Now they're paying huge amounts of money to improve the quality of what they ingest, to better shape the quality of output. What they take from Reddit, they're not blindly ingesting every comment from every user. My overall main point stands: we have viable, working, scalable mechanisms to avoid the "pollution" you're worried about. > What. Come on, I think you wouldnt agree with your own statement after reading it once more -Factual correctness is not a matter of degrees. Of course it is. An LLM can be correct 30% of the time, 80% of the time, 95% of the time, 99% of the time. If that's not a matter of degrees, I don't know what is. If you're looking for 100% perfection, I think you'll find that not even humans can do that. ;)
    - intended 2y ago
      > I think we're talking past each other Likely. Re: > we have viable, working, scalable mechanisms to avoid the "pollution" you're worried about. Do note - it’s the scalable mechanisms that I am looking at. I dont think the state of the art has shifted much since the last paper by OpenAI. Can you link me to some new information or sources that lend credence to your claim. > An LLM can be correct 30% of the time, 80% of the time, 95%… That would be the error rate, which can be a matter of degrees. However factual correctness largely cannot - the capital of Sweden today is Stockholm, with 0% variation in that answer.

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.

intended 2y ago
Thank you for being blunt. Let me attempt to speak in the same earnest tone. You are contradicting the papers and work that the people who make the models are saying. Alternatively, you are looking at the dataset curation process with rose tinted glasses. >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. Common crawl is instrumental in building our models, 60% of GPT's training data was Common Crawl. (https://arxiv.org/pdf/2005.14165 https://arxiv.org/pdf/2005.14165) pg 9. CC in turn was never intended for LLM training, this misalignment in goals results in downstream issues like hate speech, NYT content, copyrighted content and more getting used to train models. https://foundation.mozilla.org/en/research/library/generative-ai-training-data/common-crawl/ https://foundation.mozilla.org/en/research/library/generativ... (This article is to establish the issues with CC as a source of LLM training) https://facctconference.org/static/papers24/facct24-148.pdf https://facctconference.org/static/papers24/facct24-148.pdf (this details those issues.) Firms, such as the NYT are now stopping common crawl from archiving their pages. https://www.wired.com/story/the-fight-against-ai-comes-to-a-foundational-data-set/ https://www.wired.com/story/the-fight-against-ai-comes-to-a-... ----- TLDR: 'NYT' and other high quality content has largely been ingested by models. Reddit and other sources play a large part in training current models. While I appreciate your being blunt, this also means not being sharp and incisive. Perhaps precision would be required here to clarify your point. Finally - >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 What. Come on, I think you wouldnt agree with your own statement after reading it once more -Factual correctness is not a matter of degrees. Furthermore, facts dont automatically create facts. Calcuation, processing, testing and verification create more facts. Just putting facts together creates content.
- crazygringo 2y ago
  Re: corpus content, I think we're talking past each other. I'm saying that current models aren't being blindly trained on untrusted blogspam, and that there's a lot of work done to verify, structure, transform, etc. And earlier models were trained with lower-quality content, as companies were trying to figure out how much scale mattered. Now they're paying huge amounts of money to improve the quality of what they ingest, to better shape the quality of output. What they take from Reddit, they're not blindly ingesting every comment from every user. My overall main point stands: we have viable, working, scalable mechanisms to avoid the "pollution" you're worried about. > What. Come on, I think you wouldnt agree with your own statement after reading it once more -Factual correctness is not a matter of degrees. Of course it is. An LLM can be correct 30% of the time, 80% of the time, 95% of the time, 99% of the time. If that's not a matter of degrees, I don't know what is. If you're looking for 100% perfection, I think you'll find that not even humans can do that. ;)
  - intended 2y ago
    > I think we're talking past each other Likely. Re: > we have viable, working, scalable mechanisms to avoid the "pollution" you're worried about. Do note - it’s the scalable mechanisms that I am looking at. I dont think the state of the art has shifted much since the last paper by OpenAI. Can you link me to some new information or sources that lend credence to your claim. > An LLM can be correct 30% of the time, 80% of the time, 95%… That would be the error rate, which can be a matter of degrees. However factual correctness largely cannot - the capital of Sweden today is Stockholm, with 0% variation in that answer.