Back when GPT-3 was first announced I got kind of scared, and decided to download the then-current Kiwix ZIM archives of Wikipedia, Stack Overflow, Wikihow, Wikisource, and a number of other similar sites. I'm kind of glad that I did, and intend to keep these versions "forever", as examples of pre-LLM human-generated content.
Any chance you can upload those versions to archive.org?
That's a good idea. If they're not already there I will do so.
> What if we're currently in peak LLM? The moment in history where ~none of the content used to train them, and to have them operate on is aware of its LLM consumers, but from now on everything will be, and the quality of LLMs will slowly decrease? Having read the authors summary of what they mean by "Peak LLM" I do agree to an extent. As reams of shitty wordpress sites pollute the internet regurgitating GPT prompts…
This is only true if, like now, the entities controlling LLMs are research centres. I think its likely the future owners of LLMs have similar incentives to google to monetize the project.
I think LLMs will be like the steam powered toys of Ancient Rome: a curiosity that implies greater utility, but ultimately requires too many other discoveries to be made first in order to be put into practice.
> if LLM-generated content outpaces human-generated content, the useful data proportion will diminish, and in conjunction with LLM content optimization it will become exponentially harder to find useful new bits While possible and concerning, this isn’t inevitably true. To take the optimistic view, LLMs can be more than simple regurgitation machines, and can create new insights from existing knowledge. Novel/useful L…
But there’s almost no way to tell at scale which part of the training set is your novel, useful stuff and which is pure bullshit.
I have felt this train rumbling down the tracks since GPT-3 hit. He compares peak LLM to what has happened with SEO, but that doesn't really capture it. Gaming the Google algorithm has made the discovery of human-generated content more difficult, but what happens when most of the content to be found by LLM-powered search engines is itself generated by LLMs? The Internet after 2022 rapidly becomes garbage and everythi…
I wonder if we end up back at paid answer sites.
That is, any question GPT is unsure or doesn't know could be pushed into some kind of StackOverflow style q&a to resolve by real humans.
Will we be ever able to determine if LLM had peaked or not, or that it’s getting better or worse? Is there a way to tell? I mean throwing random sentences at it and try to determine that it responded right to it can’t be the way forward? And for what applications can it be trusted to do as if it just suddenly just decides to answer incredibly wrong?
I didn't realize until recently is that the "programming" of chatGPT is a hidden prompt fed into the black-box before your document is appended. * ChatGPT's "inability to separate data from code" means every input, even training input, is an eval(). * Is it now impossible to train another LLM on web input? The genie is out of the bottle--you can spam prompts into anything (webforms, html, etc) and compromise future L…
Is it now impossible to train another LLM on web input? The genie is out of the bottle--you can spam prompts into anything (webforms, html, etc) and compromise new LLMs. The only reason openAI could do it with chatGPT is that people hadn't realized it yet and spammed the input data with prompts? Wasn't that training the last "clean" dataset? Pre-2023 web crawls will be the low-background steel of future LLM training.
(Author here) that's what I thought originally, but then it means that LLMs never get to learn from new content - current ones stop in 2021, they don't know that Russia invades Ukraine, or that Arc is a cool browser or the API of any libraries released after their end date (which has been an issue for me for code generation using fast moving libraries). I don't think it's good enough to stop acquiring new content.
We're a long ways from "Peak LLM", if we will ever get there. If we are, indeed, in a virtuous cycle of LLMs building on each other, then we are actually in the knee of the curve before exponential increase in LLM capability. An LLM that can access all other AI models (e.g., HuggingGPT) is not limited to the strengths and weaknesses of any one model. Declarations of "Peak LLM" or "LLMs can never be secured" are as la…
Just imagine the amazing new colour I can make by mixing all these other ones together!
It is closer to "Look at the amazing new tool I can make using all these other ones together!"
Is it now impossible to train another LLM on web input? The genie is out of the bottle--you can spam prompts into anything (webforms, html, etc) and compromise new LLMs. The only reason openAI could do it with chatGPT is that people hadn't realized it yet and spammed the input data with prompts? Wasn't that training the last "clean" dataset? Pre-2023 web crawls will be the low-background steel of future LLM training.
(Author here) that's what I thought originally, but then it means that LLMs never get to learn from new content - current ones stop in 2021, they don't know that Russia invades Ukraine, or that Arc is a cool browser or the API of any libraries released after their end date (which has been an issue for me for code generation using fast moving libraries). I don't think it's good enough to stop acquiring new content.
phind gpt4 enabled search fixes the new content bias