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OpenAI’s policies hinder reproducible research on language models
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Re: OpenAI’s policies hinder reproducible research on language models
#32I understand any individual's company anti-competitive measures. OpenAI looks at Google the same way Apple looked at IBM in the 80s.
What I'm worried about is a lot of the talk about guarding models, public safety and misuse of models will end up leading every big company to pull public access of their APIs. We might look at 2022-2023 as a brief golden age when regular people could use stuff like GPT-4 before it was firewalled and available only to large corporations and those with personal relations to big tech execs.
Extrapolating from OpenAI's change of philosophy and business practices from their early days to now, it seems to be the way things are going. I only hope it doesn't go the way of that one paper which wanted to ban GPUs for sale to the public.
Re: OpenAI’s policies hinder reproducible research on language models
#33I'm quite sure even OpenAI themselves aren't sure if they can reproduce the current models from the scratch. Unless the computing becomes much more powerful and much cheaper, LLM is more or less a rocket science (i.e. hella expensive trial and error). It's not easy to burn lots of dollars just to get what's already there.
In this sense, it's more hacking than crareful and well specified engineering, and that could lead down a path of instability in the product where some features get better while others get worse, without understanding exactly why.
I mean pretty much all real engineering started with that time periods “hacking”/“tinkering” before thorough models and equations were derived.
We had 200 years of tinkering with relatively modern steam engine technology before Carnot and Watt started just barely scratching the surface of the first principles of thermodynamics and engine efficiency.
Even the eponymous Carnot cycle wasn’t rigorously defined mathematically during Carnot’s life. That being (T1−T2)/T1 as the temperature delta part of the equation, because absolute temperature hadn’t been accepted and defined by Lord Kelvin yet.
Some decade later the first law of thermodynamics was finally invented.
Hundreds of years of experimentation until the first principles. Machine learning has lots of control systems theory and information theory to help with analysis but we barely have an “engineering” in “software engineering” today, let alone in “machine learning engineering”. We’ll get there, but it’ll be awhile before there are proven design equations with rigorous derivations from first principle that allow us to design and build a precise AI model as surely as we can design and build a precise bridge or levee or distillation column.
Let the hacking continue, let’s not worry too much about the future “engineering” that will follow in its own time. Unless you want to discover it yourself or fund its discovery.
Re: OpenAI’s policies hinder reproducible research on language models
#34All these research science bureaucrats at Big Tech could have released LLM models or tried to develop what OpenAI did. But none of them did. We should applaud OpenAI for the innovation and let them do as they please.
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Re: OpenAI’s policies hinder reproducible research on language models
#35I'm quite sure even OpenAI themselves aren't sure if they can reproduce the current models from the scratch. Unless the computing becomes much more powerful and much cheaper, LLM is more or less a rocket science (i.e. hella expensive trial and error). It's not easy to burn lots of dollars just to get what's already there.
I don't even care if it's reproducible or not. I care it gives me correct responses to my questions and that's all.
Isn't part of making it reproducible also part of ensuring correct results? Especially if we start putting these models into important systems. And if these models begin to update in an evergreen fashion, or utilize realtime data, getting verifiable or repeatable outputs will be a nightmare if we have no idea how to make these models repeatably.
Re: OpenAI’s policies hinder reproducible research on language models
#36Earlier quoted context omitted.
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Which is great, but it is a rug pull for those who contributed to a non-profit, and a shame for open software in general. They also built their business while receiving non-profit tax breaks. I am not saying changing structure was illegal or it shouldn't be allowed to happen, but it's obvious why it's left some people disappointed.
and their first purpose is keeping human from probably damage with AI. now, there are no people treat skynet.
Re: OpenAI’s policies hinder reproducible research on language models
#37Since OpenAI didn't release the parameter count of GPT-4, I've been wondering/doubting if it is really much bigger than GPT-3. The release of GPT-3.5 has shown that they've found ways of drastically cutting down compute costs (an order of magnitude) while maintaining or even improving the quality of the model's outputs. Perhaps the reason that they didn't release the specifics of GPT-4 might be in part due to them wa…
Yannic Kilcher's opinion on this is likely correct. Similar parameter count, but trained for longer. The particulars of their instruction tuning/whatever-else-they-did are the real secret sauce.
Don't forget about a more efficient attention that let's them get 32k tokens of context.
Re: OpenAI’s policies hinder reproducible research on language models
#38Use another model
Re: OpenAI’s policies hinder reproducible research on language models
#39All these research science bureaucrats at Big Tech could have released LLM models or tried to develop what OpenAI did. But none of them did. We should applaud OpenAI for the innovation and let them do as they please.
Google (and others) may not have released model weights, but they've published papers, which is ultimately what makes the field advance. OpenAI not only did not publish any GPT4 paper, they haven't even said how many parameters it has.
Re: OpenAI’s policies hinder reproducible research on language models
#40I've been busy with a number of projects and haven't had time to look into this but have been dying to know; has anyone recreated the architecture that OpenAI uses for text-davinci-003, InstructGPT, and ChatGPT that simply doesn't have training data?
This is a reproducibility problem of its own sort. I mean, the papers are there out in the open if I understand correctly, but I don't know if anyone's actually built their own transformer architecture 1:1 against what OpenAI claims they're doing in the open.
I've seen maybe one or two models that supposedly do something similar on HuggingFace, but I'm itching to find the time to build my own.
If someone out there has already built it, I'd be fascinated to know what it looks like to train this architecture on a completely limited naive subset of knowledge that ChatGPT itself claims to be trained on:
> As an AI language model, I have been trained on a large corpus of text data from various sources, including but not limited to:
> 1. Wikipedia
> 2. Books from Project Gutenberg
> 3. Web pages from Common Crawl
> 4. News articles from various sources, including CNN, Reuters, and BBC
> 5. Academic papers from arXiv
> 6. Reddit posts and comments
> 7. Movie scripts
> 8. Song lyrics
> 9. Transcripts of speeches and interviews
> 10. User-generated content from various forums and social media platforms.
> This list is not exhaustive, and my training data is constantly updated and expanded to ensure that I can provide the most accurate and up-to-date information possible.
Like, can you imagine how a ChatGPT-like model would respond if only trained on particular discussions from subset communities online?
I think there's an interesting opportunity to basically collect communal knowledge from specific isolate communities and understand what a statistically probable output might be from particular groups of people.
It may turn particular soft science studies into hard science questions.
But you'd only know presumably if you had a working architecture with a near empty dataset.
This would also be tremendously useful for building automated chat AI for products that doesn't need to know the entirety of Clint Eastwood's career or the specific details of the features of a Boeing 747.