This is a linkpost for https://openai.com/research/gpt-4
We’ve created GPT-4, the latest milestone in OpenAI’s effort in scaling up deep learning. GPT-4 is a large multimodal model (accepting image and text inputs, emitting text outputs) that, while worse than humans in many real-world scenarios, exhibits human-level performance on various professional and academic benchmarks.
Full paper available here: https://cdn.openai.com/papers/gpt-4.pdf
I want to highlight that in addition to the main 98-page "Technical Report," OpenAI also released a 60-page "System Card" that seems to "highlight safety challenges" and describe OpenAI's safety processes. Edit: as @vladimir_nesov points out, the System Card is also duplicated in the Technical Report starting on page 39 (which is pretty confusing IMO).
I haven't gone through it all, but one part that caught my eye from section 2.9 "Potential for Risky Emergent Behaviors" (page 14) and shows some potentially good cross-organizational cooperation:... (read more)
It seems pretty unfortunate to me that ARC wasn't given fine-tuning access here, as I think it pretty substantially undercuts the validity of their survive and spread eval. From the text you quote it seems like they're at least going to work on giving them fine-tuning access in the future, though it seems pretty sad to me for that to happen post-launch.
More on this from the paper:
Beth and her team have been working with both Anthropic and OpenAI to perform preliminary evaluations. I don’t think these evaluations are yet at the stage where they provide convincing evidence about dangerous capabilities—fine-tuning might be the most important missing piece, but there is a lot of other work to be done. Ultimately we would like to see thorough evaluations informing decision-making prior to deployment (and training), but for now I think it is best to view it as practice building relevant institutional capacity and figuring out how to conduct evaluations.
Sub-Section 2.9 should have been an entire section. ARC used GPT-4 to simulate an agent in the wild. They gave GPT-4 a REPL, the ability to use chain of thought and delegate to copies of itself, a small amount of money and an account with access to a LLM api. It couldn't self replicate.
I think it's important for ARC to handle the risk from gain-of-function-like research carefully and I expect us to talk more publicly (and get more input) about how we approach the tradeoffs. This gets more important as we handle more intelligent models, and if we pursue riskier approaches like fine-tuning.
With respect to this case, given the details of our evaluation and the planned deployment, I think that ARC's evaluation has much lower probability of leading to an AI takeover than the deployment itself (much less the training of GPT-5). At this point it seems like we face a much larger risk from underestimating model capabilities and walking into danger than we do from causing an accident during evaluations. If we manage risk carefully I suspect we can make that ratio very extreme, though of course that requires us actually doing the work.
Blog post with more details on the evals we did is now up here. We plan to publish a detailed description of our evaluation methodology and results soon, blog post just gives high-level description.... (read more)
More details on methodology:
Potential dangers of future evaluations / gain-of-function research, which I'm sure you and Beth are already extremely well aware of:
Ahh, I see. You aren't complaining about the 'ask it to do scary thing' part, but the 'give it access to the internet' part.
Well, lots of tech companies are in the process of giving AIs access to the internet; ChatGPT for example and BingChat and whatever Adept is doing etc. ChatGPT can only access the internet indirectly, through whatever scaffolding programs its users write for it. But that's the same thing that ARC did. So ARC was just testing in a controlled, monitored setting what was about to happen in a less controlled, less monitored setting in the wild. Probably as we speak there are dozens of different GPT-4 users building scaffolding to let it roam around the web, talk to people on places like TaskRabbit, etc.
I think it's a very good thing that ARC was able to stress-test those capabilities/access levels a little bit before GPT-4 and the general public were given access to each other, and I hope similar (but much more intensive, rigorous, and yes more secure) testing is done in the future. This is pretty much our only hope as a society for being able to notice when things are getting dangerous and slow down in time.
Confirmed: the new Bing runs on OpenAI’s GPT-4 | Bing Search Blog
But is it the same, full-sized GPT-4 with different fine-tuning, or is it a smaller or limited version?
I called it explicitly in many places and many ways, but one of my favorite is this meme
Wow, that's good, right?
Yes. How good is up for debate, but it's definitely good.
You can definitely infer quite a bit from the paper and authors by section, but there is a big difference between a plausible informed guess, and knowing. For most purposes, weak inferences are not too useful. 'Oh, this is Chinchilla, this is VQ-VAE, this is Scaling Transformer...' For example, the predicting-scaling part (and Sam Altman singling out the author for praise) is clearly the zero-shot hyperparameter work, but that's not terribly helpful, because the whole point of scaling laws (and the mu work in particular) is that if you don't get it right, you'll fall off the optimal scaling curves badly if you try to scale up 10,000x to GPT-4 (never mind the GPT-5 OA has in progress), and you probably can't just apply the papers blindly - you need to reinvent whatever he invented since and accumulate the same data, with no guarantee you'll do it. Not a great premise on which to spend $1b or so. If you're a hyperscaler not already committed to the AI arms race, this is not enough information, or reliable enough, to move the needle on your major strategic decision. Whereas if they had listed exact formulas or results (especially the negative results), it may be enough of a roadmap to kickstart another competitor a few months or years earlier.
Why doesn't it improve on AP English Literature and AP English Language?
I don't have a good guess, but I found the AP English Language exam description with example questions and grading procedures if anyone wants to take a look.
How is it that bad at codeforces? I competed a few years ago, but in my time div 2 a and b were extremely simple, basically just "implement the described algorithm in code" and if you submitted them quickly (which I expect gpt-4 would excel in) it was easy to reach a significantly better rating than the one reported by this paper.
I hope they didn't make a mistake by misunderstanding the codeforces rating system (codeforces only awards a fraction of the "estimated rating-current rating" after a competition, but it is possible to exactly calculate the rating equivalent to the given performance from the data provided if you know the details (which I forgot))
When searching the paper for the exact methodology (by ctrl-f'ing "codeforces"), I haven't found anything.
I know. I skimmed the paper, and in it there is a table above the chart showing the results in the tasks for all models (as every model's performance is below 5% in codeforces, on the chart they overlap). I replied to the comment I replied to because thematically it seemed the most appropriate (asking about task performance), sorry if my choice of where to comment was confusing.
From the table:
GPT-3.5's codeforces rating is "260 (below 5%)"
GPT-4's codeforces rating is "392 (below 5%)"
They did run the tests for all models, from Table 1:
(the columns are GPT-4, GPT-4 (no vision), GPT-3.5)
Gonna pull out one bit from the technical report, section 2.12:... (read more)
On page 2 of the system card it says:
(Emphasis added.) This coincides with the "eight months" of safety research they mention. I wasn't aware of this when I made my original post so I'll edit it to be fairer.
But this itself is surprising: GPT-4 was "finished training" in August 2022, before ChatGPT was even released! I am unsure of what "finished training" means here - is the released model weight-for-weight identical to the 2022 version? Did they do RLHF since then?
What??? This is so weird and concerning.
Not a new phenomenon. Fine-tuning leads to mode collapse, this has been pointed out before: Mysteries of mode collapse
“However, through our current post-training process, the calibration is reduced.” jumped out at me too.
My guess is that RLHF is unwittingly training the model to lie.
If I ask a question and the model thinks there is an 80% the answer is "A" and a 20% chance the answer is "B," I probably want the model to always say "A" (or even better: "probably A"). I don't generally want the model to say "A" 80% of the time and "B" 20% of the time.
In some contexts that's worse behavior. For example, if you ask the model to explicitly estimate a probability it will probably do a worse job than if you extract the logits from the pre-trained model (though of course that totally goes out the window if you do chain of thought). But it's not really lying---it's also the behavior you'd expect out of a human who is trying to be helpful.
More precisely: when asked a question the pre-trained model outputs a probability distribution over what comes next. If prompted correctly you get its subjective probability distribution over the answer (or at least over the answer that would appear on the internet). The RLHF model instead outputs a probability distribution over what to say take next which is optimized to give highly-rated responses. So you'd expect it to put all of its probability mass on the best response.
Yes, I think you are misunderstanding figure 8. I don't have inside information, but without explanation "calibration" would almost always mean reading it off from the logits. If you instead ask the model to express its uncertainty I think it will do a much worse job, and the RLHF model will probably perform similarly to the pre-trained model. (This depends on details of the human feedback, under a careful training regime it would probably get modestly better.)
I think this would be a surprising result if true, and I suspect it would be taken as a significant problem by researchers at OpenAI.
Someone should submit the few safety benchmarks we have if they haven't been submitted already, including things like:
Am I missing others that are straightforward to submit?
The developers are doing a livestream on Youtube at 1PM PDT today:
tldw: Brockman showed up some straightforward demos of GPT-4's text & code writing versatility, and some limited demo of its image processing, but you aren't missing anything insightful about the arch/training/scaling/future/etc.
So Bing was using GPT-4 after all. That explains why it felt noticeably more capable than chatGPT. Still, this advance seems like a less revolutionary leap over GPT-3 than GPT-3 was over GPT-2, if Bing's early performance is a decent indicator.
In chess, which I find to be a useful test of LLM capability because (a) LLMs are not designed to do this and (b) playing well beyond the opening requires precision and reasoning, I would say GPT4 is roughly at least weak, possibly intermediate club player level now. This is based on one full game, where it played consistently well except for making a mistake in the endgame that I think a lot of club players would also have made.
It seems better at avoiding blunders than Bing, which could be due to modifications for search/search-related prompting in Bing. Or it could be random noise and more test games would show average level to be weaker than the reported first impression.
Bumping someone else's comment on the Gwern GPT-4 linkpost that now seems deleted:
This does seem like quite a significant jump (among all the general capabilities jumps shown in the rest of the paper. The previous SOTA was only 75.2% for Flan-PaLM (5-shot, finetuned, CoT + SC).
And that previous SOTA was for a model fine-tuned on MMLU, the few-shot capabilities actually jumped from 70.7% to 86.4%!!
This is up from ~4k tokens for davinci-text-003 and gpt-3.5-turbo (ChatGPT). I expect this alone will have large effects on the capabilities of many of the tools that are built on top of existing GPT models. Many of these tools work by stuffing a bunch of helpful context into a prompt, or chaining together a bunch of specialized calls to the underlying LLM using langchain. The length of the context window ends up being a pretty big limitation when using these methods.
Any idea how many parameters it has?
This non-news seems like it might be the biggest news in the announcement? OpenAI is saying "oops publishing everything was too open, its gonna be more of a black box now".
So apparently there are formalized personality-specifying prompts now, making it not a particular simulacrum, but a conditioning-controlled simulacrum generator. This also explains what the recent mysteriously vague blog post was about.
GPT-3 was horrible at Morse code. GPT-4 can do it mostly well. I wonder what other tasks GPT-3 was horrible at that GPT-4 does much better.
Scaling laws work predictably. There is plenty of room for improvement should anyone want to train these models longer, or presumably train larger models.
The model is much more calibrated before fine-tuning/RLHF, which is a bad sign for alignment in general. Alignment should be neutral or improve calibration for any kind of reasonable safety.
GPT-4 is just over 1-bit error per word at predicting its own codebase. That's seems close to the capability to recursively self-improve.
You can just append
#page=3. This works in most PDF viewers. (There are many query parameters that Adobe supports but that's really the only one you need to know about.)
Does anyone here have any granular takes what GPT-4's multimodality might mean for the public's adoption of LLMs and perception of AI development? Additionally, does anyone have any forecasts (1) for when this year (if at all) OpenAI will permit image output and (2) for when a GPT model will have video input & output capabilities?
We can give a good estimate of the amount of compute they used given what they leaked. The supercomputer has tens of thousands of A100s (25k according to the JP Morgan note), and they trained firstly GPT-3.5 on it 1 year ago and then GPT-4. They also say that they finish the training of GPT-4 in August, that gives a 3-4 months max training time.
25k GPUs A100s * 300 TFlop/s dense FP16 * 50% peak efficiency * 90 days * 86400 is roughly 3e25 flops, which is almost 10x Palm and 100x Chinchilla/GPT-3.
Would be interesting to see the transcripts on harder questions altered enough to exclude the possibility of being in the training set.
Edit: That is, it's the originally composed (rather than remembered) transcripts themselves that I'm interested in seeing, not as a way of verifying the score. Like, with writing a quine the interesting thing is that there is no internal monologue to support the planning of how the code gets written. I wouldn't be able to solve this problem without a bit of planning and prototyping, even if it takes pl... (read more)
Has this happened yet? Or is this about plans for the near future?
Edit: Apparently there are currently technical issues and it already works for some people (on ChatGPT+), just not for everyone.
Did gpt 3.5 get high scores on human exams before fine tuning? My rough impression is “gpt4 relies less on fine tuning for its capabilities”
So first thoughts while reading the research/Gpt-4 page
- ChatGPT System Prompt open soon to users, not API holders, that's going to be interesting.
-Only trained adversarialy with 50 experts? One would think you would spend a bit more and do it with 100x more people? Or if hard to coordinate, at least 500.
Closing in on human performance, but I would love to see numbers on the compute needed to train. They can predict loss accurately, but what that loss means seems to be mostly an open problem.
Will need a couple days and rereads to digest this.
If you buy a pro-subscription to ChatGPT, can use you GPT-4 the same way one would have used the 3.5 engine? Does anyone have made interesting experiences with it?
Did ARC try making a scaling plot with training compute on the x-axis and autonomous replication on the y-axis?