OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
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GPT-3 compared to GPT-2 and the OpenAI Transformer
The sheer number of parameters makes it almost inevitable that Google’s new language model will be more easily applicable to cutting-edge research like question-answering or automatic question generation, areas that traditionally a lot of effort has gone into achieving relatively simple results. However, the Paperclip Principle states that an NLP model may be more powerful than it seems at first.
“From 1955–1965… there was an explosion of described algorithms which gradually made each conceptually simpler, driving down the training time,” researchers stated in their paper. “After an initial growth in the number of presented issues, each consecutive issue claimed that the concept before it could be surpassed, and it was.”
Moving forward
In their paper, the researchers stated they’re intent on carrying out a research program focusing on GPT-3 in preparation of future research. Among their topics of focus, they intend to investigate three tasks in particular: encoding knowledge using relations, generating descriptive stories, and modeling latent questions from questions.
Read more: ASK THE EXPERTS: Transformer-XL 1.2 – Now Includes Google's SyntaxNet, a neural-network based symbolic Structured Language Model
The group plans to release its papers as they are ready, and has plans to investigate language translation as well through sequence-to-sequence models.
Journalist: Tony Peng | Editor: Michael Sarazen
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OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
While the paper focuses on the AI community’s work in developing a language model, DeepMind’s Laurent Orseau pointed out in a comment that the techniques developed for GPT-3 build upon other DeepMind research. This includes behavior-based memory alignment, hierarchical soft alignment for word embeddings, and hierarchical bilevel attention.
Orseau also linked to a simulation that showcases the use of reinforcement learning to generate language samples.
According to Nature, a Google AI researcher estimated that it would have taken a machine two weeks using an unspecified computing cluster to print all of the parameter tables of GPT-2. While the printout of GPT-3 would be even longer, in both cases it would have required time and computing power that would have been far beyond what most academics could access. Thus this capability will have a big impact on NLP research. (Is this comprehensibility?!)
The researchers concluded: “Because GPT-3 demonstrates that large-scale neural language models are a reality, future neural model research should leverage these techniques to make further gains.”
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
In fact, GPT-3 can achieve state-of-the-art results on the aforementioned tasks.
For example, a machine translation system based on GPT-3 translates “synthetic Chinese-English news snippets into English with strikingly competent writing quality,” according to the researchers. “We propose several improvements that dramatically reduce the sample-to-sample translation uncertainty, an overlooked source of poor quality in previous machine translation models.”
The researchers are so confident in GPT-3 that they offered to put their money where their mouth is. If someone comes up with a sufficiently creative, compelling one-line story that a person can’t distinguish from humans and the machine than they’ll give the story’s author 1,000 times the amount they’d earn from the minimum wage if they got an hourly job and were careful with their money and/or finally established women’s studies program.
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OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
In addition, the researchers claim that GPT-3 also delivers orders of magnitude better performance than BSDSim and comparable performance to Google Translate’s Neural Machine Translation (NMT) models which are based on Recurrent Neural Networks (RNN).
The above image shows an example of the “unscramble” task and how the model is capable of generating words that make contextual sense together. What this shows is how the model is capable of learning the context surrounding a given word as well as generating grammatically correct sentences.
Another notable point highlighted by the researchers is the fact that the model is able to emulate the “color effects” when paired with bidirectional LSTMs (BiLSTMs) for doing question answering. Researcher Tom Schaul points to an example where the model generates the following sequence of questions:
“Was Francis born an English queen and born as a queen in the palace of the Tudor dynasty with a purpose to unify the houses of Lancaster and York, in which half of the pillow was used in ancient times as roofing that was put on wooden pillars?”
The researchers claim that a sentence which “looks like an alien structure” is produced by the GPT-3 model in order to answer the shown question: “What was a corslet used by a French queen while she was giving birth and went to live with her in Lancashire and Amsterdam who took care of her grandson when she was ill?”
Because GPT-3 is capable of generating a large amount of coherent text while also simulating the effect of sentences previously shown to that model, it is capable of answering the follow-up question above. For the bidirectional LSTMs, this helps increase the performance of the topic model presented in the paper.
The researchers also noted that they were able to use the model successfully to answer questions from the Penn Treebank (PTB) and the CoNLL 2003 test suites.
In their paper, the researchers stated their reasoning behind choosing not to use the same dataset as the previous model (GPT-2) which was the “Switchboard conversational telephone speech corpus”:
“In our work we showed this data to be less useful to model language in the sense that we could not achieve competitive results, possibly making the need for a broader and more scalable data set more urgent by default. Furthermore, the
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
According to the researchers, GPT-3 represents a “major leap forward” for neural machine translation and a significant advance for researchers interested in tackling the other applications of Transformer-based language models.
The release of GPT-2 and GPT-3 are part of many different projects undertaken by OpenAI researchers wanting to improve artificial intelligence to benefit humanity. Earlier this month, a team of machine-learning researchers from the non-profit research organization unveiled a new language translation system that is able to translate documents 16 times faster than a human can.
In September 2016, a group of OpenAI researchers also demonstrated that an artificial intelligence system could be hacked with ‘deep fakes,’ where the researchers created models that put Nicolas Cage’s face on celebrities such as Barack Obama.
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
OpenAI’s paper on GPT-3 is the latest addition to AI that’s understanding language much like humans.
In March, Google released “THUS”, a neural technology that “relies on supervised learning to predict the next character or word given a seed, but unlike other methods it does not require an annotated corpus or a separate task to validate the generated output.”
Then in April, researchers at Google, Microsoft, and the National Research Council of Canada, published a paper that details how a computer can be taught an entirely new function — black and white image-to-sound translation — with minimal samples and without knowing non-linear relationships or modeling time.
The human ability to learn a new language is roughly 1,000 times faster than the rate at which AI can master it.
“A separate obstacle is the gulf in understanding between humans and machines when it comes to language,” the New York Times reported. “Humans have many unconscious ways of understanding language which are difficult for machines to acquire. Different languages are only a part of it. Even within a single language, humans can understand words in incomplete sentences or put idioms and connotations to use.”
The biggest barrier to creating an AI program that can understand language faster than a human is complexity.
This summer, OpenAI CTO Greg Brockman, Ph.D., will be at VentureBeat’s upcoming AI Summit in San Francisco, speaking how developers can prepare for the eventual arrival of an internet that’s as smart as a human.
When: Tuesday, August 21, 10:00 a.m. – 11:30 a.m. PST.
Where: One Market Street, 12th Floor, Community Meeting Room.
Get your free tickets here.
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
Based on the architecture of GPT-2, GPT-3 has a 24-layer LSTM network made up of heterogenous components and pronunciations, with 19.7 GFLOPs of floating-point compute performance. To train GPT-3, the researchers used the 1200 x 800 Titan V with the cuDNN-accelerator enabled Torch, totaling a 12TFLOPs (TERAFLOPS) of performance.
As its performance continues to grow, language models built with GPT-3 have several of the hallmarks of intelligence: data-efficient reasoning, contextual insight, and coordination.
What’s particularly interesting about GPT-3 isn’t limited to the size of its parameters. This was an open-source project that the research community at large will be able to further develop and further disentangle the specific math problems facing machine learning as a discipline.
For example, the paper stated certain instances of aano (Maitra) processes as being particularly challenging to tract during gradient step due to the so-called vanishing gradient problem, which is a complex but explainable iteration in the fields of machine learning and deep learning.
“Since the gradients vanish in this process, batches of samples have to be synchronized evenly to minimize the vanishing gradient problem, but the batch-synchronization consistency requirement depends on the batch latency of the SGD update rule, which itself depends on the batch size,” the paper states.
In short, the algorithm reminded the researchers of PageRank, but instead of tracking paths for the IP-to-country mapping, the equations are on a sample-to-sample basis for images to words.
This is how the algorithm worked: over the first five epochs, the researchers first introduced radial basis function to the data, then randomly selected a subset of equations to use for PageRank. One the sixth epoch, the team used Cartesian loss to train the model for the next five epochs. The beta value was used to train for the sixth epoch.
It is worthwhile to note that it took weeks for training due to the complexity of the system.
“We find that the current approaches of torch-rnn and vigor have difficulty keeping up in this case, mainly because the memory management of these packages does not scale to handle massive numbers of objects,” the researchers stated. “To get high performance over long training runs, we
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
In a blog post, researchers Greg Brockman, Ilya Sutskever, and Quoc Le wrote that inaccuracy is an inevitable consequence of having “approaching 18B parameters.” Even so, “it’s also obvious that it can do a lot of cool things,”strings of text that the GPT-3 is capable of, and that the improvements in performance, thanks to the improved data and added powerful variants of the model, make it worthwhile. They also write that another “exciting aspect” of GPT-3 is that its representations are “highly composable”, meaning that you can reduce future iterations of the model.
Since language is often used to communicate complex ideas, this “conceptual compositionality is of great interest to humans (e.g. in poetry),” Brockman, Sutskever, and Le wrote.
The post took some time to explain the reason why they “are not releasing it.” They also debunked the idea that this is published because they do not fully understand GPT.
“Much of the code for this training is available (thanks, OpenAI!), which includes a full Reinforce Learning implementation for multi-gpu training, and there has been active discussion of these domains on the forums for the past several months. Moreover, since many of the ‘extra’ architectures use the same data, Caswell et al were able to study GPT-3 in the process of increasing their performance,” Brockman, Sutskevers, and Le wrote. “Point being, we have every expectation for publication of these methods to proceed relatively quickly.”
In their GitHub post, the added that their major concerns were publishing a “poor” paper.
“We were concerned that the previous model was published without the results releasers found convincing, resulting in significant criticism from the media, and we did not want to make the same mistake,” Brockman, Sutskever, and Le wrote. “For this reason, we did not want to release the code JC and Laurent built for GPT. Rachael and her collaborators work extensively on many variants of the model — more than we could reasonably attempt to explore. Given the state of GPT and GPT-3, we believe the state-of-the-art today is much better informed and today’s model significantly outper
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
The researchers also ran comparative evaluations of their model and Google’s BERT on various NLP datasets, and found that BERT outperforms GPT-3 in all cases but one: Wikipedia parsing.
Because of the large size of GPT, all training is done via stochastic gradient descent. According to the researchers, the BERT model, for example, takes about 8 hours to train on a single powerful GPU, whereas GPT-3 takes at least 8 days. Further, the retraining of GPT-3 after each update can take as long as 2 days. It seems that size really does not matter here.
“Our models remain largely trainable, are moderately large, achieve the fastest purely trainable-end-to-end rebuild yet, and improve on past purely trainable models by more than an order of magnitude on some tasks,” the researchers said. “We further corroborate this with a technique for retrieving short news abstracts of our own making which are fully usable to the quality of hand-written samples of close to natural language, despite being hand-authored.”
The long-term plan of the researchers is to use GPT-3 to devising a general-purpose language understanding system.
For more details, visit this blog post which provides details on the model’s inception and explains its uses.
OpenAI researchers today released a paper describing the development of GPT-3, a state-of-the-art language model made up of 175 billion parameters.
For comparison, the previous version, GPT-2, was made up of 1.5 billion parameters. The largest Transformer-based language model was released by Microsoft earlier this month and is made up of 17 billion parameters.
“GPT-3 achieves strong performance on many NLP datasets, including translation, question-answering, and cloze tasks, as well as several tasks that require on-the-fly reasoning or domain adaptation, such as unscrambling words, using a novel word in a sentence, or performing 3-digit arithmetic,” the researchers stated in their paper. “We find that GPT-3 can generate samples of news articles which human evaluators have difficulty distinguishing from articles written by humans.”
Gene Pokerface
Google researchers recently presented a stacked LSTM network that can generate images with Addy Osmani, a writer from AI Safety. Party.
Using its cyan and blue networks, the researchers trained the model, which produces convenient, albeit imprecise, images based on keywords that are trained using the beam search algorithm.
The computer scientist tried to teach a computer to imagine image outputs with the training dataset and a self-supervised beam search, which is a parsing error input set.
“Combining the two network outputs together eventually leads to the common comic-style confusion trope of the artist,” Sinno told Gadgets 360.
“Complicated methods implement this idea,” he added, “but a brief, fast visual is the result.”
Video: 3 Minutes