4 ms·

They learned until convergence, which basically is the point where more training doesn't seem to improve output anymore. At which point they call it completed.

They learned until convergence, which basically is the point where more training doesn't seem to improve output anymore. At which point they call it completed. And that is the best it can ever be. It will never get better or be made to be better unless the entire system is scrapped and retrained. At any point though, an LLM that you can interact with is static. And that is my point. They can't improve once they are "born". And I do not see any feasible way for them to fix this as it is inherently a feature of the LLMs.

skissane 4y ago
ChatGPT has a “knowledge-cutoff”. What if you continually fed new information (like a subscription to a news wire service) into the training process so the knowledge cutoff continually moved forward? LLMs do support additional training (fine-tuning). Why couldn’t that fine-tuning be a continual process, absorbing new information every day? What if OpenAI took all their records of public interaction with ChatGPT, and used that to fine-tune ChatGPT? In fact, given upvote/downvote and “regenerate response” allow users to give feedback on responses, maybe that is part of their plan?
- Yoric 4y ago
  > ChatGPT has a “knowledge-cutoff”. What if you continually fed new information (like a subscription to a news wire service) into the training process so the knowledge cutoff continually moved forward? > LLMs do support additional training (fine-tuning). Why couldn’t that fine-tuning be a continual process, absorbing new information every day? Definitely sounds possible. However, I suspect that it would be extremely expensive in terms of energy requirements: ChatGPT 3 training has already been evaluated to be equivalent to ~100k days of energy use of an average household in the west, iirc, and ChatGPT 4 training was undoubtedly worse. I don't know how much energy it costs to fine-tune a LLM. But I suspect that what you're proposing would involve a cost similar to training ChatGPT 4, except every few iterations of fine-tuning. > What if OpenAI took all their records of public interaction with ChatGPT, and used that to fine-tune ChatGPT? In fact, given upvote/downvote and “regenerate response” allow users to give feedback on responses, maybe that is part of their plan? Given the adversarial nature of Internet, it sounds like this would be used very quickly to poison ChatGPT. Plus, let's face it, there are good chances that we, as users of ChatGPT, are actually worse than the hand-curated sources that were picked to teach ChatGPT.
oezi 4y ago
I think this is way too simplistic. First, LLMs seem to be still on a trajectory of more parameters is better. So the convergence is a man-made decision to stop for the sake of memory usage and money spend on training. I don't think there is anything inherent in LLMs which prohibit adjusting weights over time or branch out to another model which is more regularly updated and contains novel stuff. I would well imagine that they integrate a "news model" which is trained every 24h to incorporate text scraped from news-sites. This can be called from the main LLM. If we look at the human brain we realize that there are dedicated systems for specialized processing (think spatial reasoning vs judging colors). I would imagine that OpenAI would eventually to have a similar more complex system structure.
- tomohelix 4y ago
  A more parameter LLM is basically a new model that you have to train from scratch. You can't pick up say GPT3 and turn it into Chinchilla-grade LLMs with 175B params with just the difference in training between the two. The former has "converged" and more effort and money put into it would bring negligible benefits. The progress is basically reaching an asymptote. And of course there is nothing saying future progress won't make a new model that can learn on the go. But right now, the transformer structure that made LLMs such a huge thing is inherently incapable of learning from its own output. Or at least, it isn't really feasible with how demanding the cost to it would be.

skissane 4y ago

ChatGPT has a “knowledge-cutoff”. What if you continually fed new information (like a subscription to a news wire service) into the training process so the knowledge cutoff continually moved forward? LLMs do support additional training (fine-tuning). Why couldn’t that fine-tuning be a continual process, absorbing new information every day? What if OpenAI took all their records of public interaction with ChatGPT, and used that to fine-tune ChatGPT? In fact, given upvote/downvote and “regenerate response” allow users to give feedback on responses, maybe that is part of their plan?

Yoric 4y ago
> ChatGPT has a “knowledge-cutoff”. What if you continually fed new information (like a subscription to a news wire service) into the training process so the knowledge cutoff continually moved forward? > LLMs do support additional training (fine-tuning). Why couldn’t that fine-tuning be a continual process, absorbing new information every day? Definitely sounds possible. However, I suspect that it would be extremely expensive in terms of energy requirements: ChatGPT 3 training has already been evaluated to be equivalent to ~100k days of energy use of an average household in the west, iirc, and ChatGPT 4 training was undoubtedly worse. I don't know how much energy it costs to fine-tune a LLM. But I suspect that what you're proposing would involve a cost similar to training ChatGPT 4, except every few iterations of fine-tuning. > What if OpenAI took all their records of public interaction with ChatGPT, and used that to fine-tune ChatGPT? In fact, given upvote/downvote and “regenerate response” allow users to give feedback on responses, maybe that is part of their plan? Given the adversarial nature of Internet, it sounds like this would be used very quickly to poison ChatGPT. Plus, let's face it, there are good chances that we, as users of ChatGPT, are actually worse than the hand-curated sources that were picked to teach ChatGPT.

oezi 4y ago

I think this is way too simplistic. First, LLMs seem to be still on a trajectory of more parameters is better. So the convergence is a man-made decision to stop for the sake of memory usage and money spend on training. I don't think there is anything inherent in LLMs which prohibit adjusting weights over time or branch out to another model which is more regularly updated and contains novel stuff. I would well imagine that they integrate a "news model" which is trained every 24h to incorporate text scraped from news-sites. This can be called from the main LLM. If we look at the human brain we realize that there are dedicated systems for specialized processing (think spatial reasoning vs judging colors). I would imagine that OpenAI would eventually to have a similar more complex system structure.

tomohelix 4y ago
A more parameter LLM is basically a new model that you have to train from scratch. You can't pick up say GPT3 and turn it into Chinchilla-grade LLMs with 175B params with just the difference in training between the two. The former has "converged" and more effort and money put into it would bring negligible benefits. The progress is basically reaching an asymptote. And of course there is nothing saying future progress won't make a new model that can learn on the go. But right now, the transformer structure that made LLMs such a huge thing is inherently incapable of learning from its own output. Or at least, it isn't really feasible with how demanding the cost to it would be.