GPT-4 is bigger and better than ChatGPT—but OpenAI won’t say why
OpenAI has finally unveiled GPT-4, the San Francisco-based company’s next-generation large language model. Its last surprise hit, ChatGPT, was always going to be a hard act to follow, but the company has made GPT-4 even bigger and better.
Yet how much bigger and why it’s better, OpenAI won’t say. GPT-4 is the most secretive release the company has ever put out, marking its full transition from non-profit research lab to for-profit tech firm.
“That’s something that, you know, we can’t really comment on at this time,” says OpenAI’s chief scientist Ilya Shylacter when I spoke to the GPT-4 team in a video call an hour after the announcement. “It’s pretty competitive out there.”
Access to GPT-4 will be available to users who sign up to the waitlist and for subscribers of the premium paid-for ChatGPT Plus in a limited, text-only capacity.
GPT-4 is a multimodal large language model, which means it can respond to both text and images. Give it a photo of the contents of your fridge and ask it what you could make, and GPT-4 will try to come up with recipes that use the pictured ingredients.
“The continued improvements along many dimensions are remarkable,” says Oren Etzioni at the Allen Institute for AI. “GPT-4 is now the standard by which all foundation models will be evaluated.”
“A good multimodal model has been the holy grail of many big tech labs for the past couple of years,” says Thomas Wolf, co-founder of Hugging Face, the AI start-up behind the open-source large language model BLOOM. “But it has remained elusive”
In theory, combining text and images could allow multimodal models to understand the world better. “It might be able to tackle traditional weak points of language models, like spatial reasoning,” says Wolf. But it is not yet clear if that’s true for GPT-4.
OpenAI’s new model appears to be better at basic reasoning than ChatGPT, solving simple puzzles such as summarizing blocks of text using words that start with the same letter. In my demo, I was shown it summarizing the GPT-4 blurb from OpenAI’s website using words that begin with g: “GPT-4, groundbreaking generational growth, gains greater grades. Guardrails, guidance, and gains garnered. Gigantic, groundbreaking, and globally gifted.” In another demo, GPT-4 took in a document about taxes and answered questions about it alongside reasons for its responses.
It also outperforms ChatGPT on human tests, including the Uniform Bar Exam (where GPT-4 ranks in the 90th percentile and ChatGPT ranks in the 10th) and the Biology Olympiad (where GPT-4 ranks in the 99th percentile and ChatGPT ranks in the 31st). : “It’s exciting how evaluation is now starting to be conducted on the very same benchmarks that humans use for themselves,” says Wolf.
According to OpenAI, GPT-4 performs better than ChatGPT, which was based on a version of the firm’s previous technology, GPT-3, because it is a larger model with more parameters (the values in a neural network that get tweaked during training). This follows an important trend that the company discovered with its previous models. GPT-3 outperformed GPT-2 because it was more than 100 times larger, with 175 billion parameters compared to GPT-2’s 1.5 billion. “That fundamental formula has not really changed much for years,” says Jakub Pachocki, one of GPT-4’s developers. “But it’s still like building a spaceship, where you need to get all these little components right and make sure none of it breaks.”
But OpenAI has chosen not to reveal how large GPT-4 is. Unlike with its previous releases, OpenAI is giving away nothing about how GPT-4 was built—not the data, the amount of computing power or the training techniques. “OpenAI is now a fully closed company with scientific communication akin to press releases for products,” says Wolf.
OpenAI says it spent six months making GPT-4 safer and more accurate. According to the company, GPT-4 is 82% less likely than GPT-3.5 to respond to requests for content that OpenAI does not allow, and 60% less likely to make stuff up.
OpenAI says it achieved these results using the same approach it took with ChatGPT, using reinforcement learning via human feedback. This involves asking human raters to score different responses from the model and using those scores to improve future output.
The team also used GPT-4 to improve itself, asking it to generate inputs that led to biased, inaccurate or offensive responses and then fixing the model so that it refused such inputs in future.
GPT-4 may be the best multimodal large language model yet built. But it is not in a league of its own, as GPT-3 was when it first appeared in 2020. A lot has happened in the last three years. Today GPT-4 sits alongside other multimodal models, including Flamingo from DeepMind. Hugging Face is also working on an open-source multimodal model of its own that will be free for others to use and adapt, says Wolf.
Faced with such competition, OpenAI is treating this release more as a product tease rather than a research update. Early versions of GPT-4 have been shared with some of OpenAI’s partners, including Microsoft, which confirmed today that it used a version of GPT-4 to build Bing Chat. OpenAI is also now working with Stripe, Duolingo, Morgan Stanley and the government of Iceland (which is using GPT-4 to help preserve the Icelandic language) among others.
Many other companies are waiting in line: “The costs to bootstrap a model of this scale is out of reach for most companies but the approach taken by OpenAI has made large language models very accessible to startups,” says Sheila Gulati, co-founder of investment firm Tola Capital. “This will catalyze tremendous innovation on top of GPT-4.”
And yet large language models remain fundamentally flawed. GPT-4 can still generate biased, false and hateful text; it can also still be hacked to bypass its guardrails. OpenAI has improved this technology, but it has not fixed it by a long shot. The company claims that its safety testing has been sufficient for GPT-4 to be used in third party apps.
But it is also braced for surprises. “Safety is not a binary thing, it is a process,” says Shylacter. “Things get complicated any time you reach a level of new capabilities.A lot of these capabilities are now quite well understood, but I’m sure that some will still be surprising.”
Even Shylacter suggests that going slower with releases might sometimes be preferable: “It would be highly desirable to end up in a world where companies come up with some kind of process that allows for slower releases of models with these completely unprecedented capabilities.”
