Image credit: Getty Images via The Guardian
On May 28, OpenAI released the paper “Language Models are Few-Shot Learners,” which introduced GPT-3, the largest2 language model3 ever created. The 73-page paper describes how GPT-3 follows recent trends on significantly advancing the state of the art in language modeling. Broadly, on natural language processing (NLP) benchmarks, GPT-3 achieves promising, and sometimes competitive, results.
GPT-3 represents the increase in performance that comes from using a larger model, and it also follows the immense increases in model and data size that characterize recent developments in NLP. The paper’s core message however was less about its performance on benchmarks, and more about the discovery that due to its scale GPT-3 is capable of solving NLP tasks that it has never before encountered after seeing just one or a few examples of the task (‘few-shot’ learning). This is in contrast to what is typically done today, which is that models are re-trained (or ‘fine-tuned’) on a larger amount of additional data in order to perform new tasks.
Last year, OpenAI developed GPT-2, which was able to generate long, coherent texts that, on first glance, are difficult to distinguish from human writing. OpenAI notes that they “use the same model and architecture as GPT-24, but the size of the network itself, as well as the data that it was trained on5, is much larger than its predecessor – GPT-3 has 175 billion parameters compared to GPT-2’s 1.5 billion, and was trained on 570 billion gigabytes of text, while GPT-2 was trained on 40. While just increasing the scale in this way was not in itself novel, the new model’s ability to perform few-shot learning was a novel discovery, and was demonstrated in the paper via a variery of traditional Natural Language Processing tasks:
Many similar demonstrations on Twitter soon followed:
Words → website ✨— Jordan Singer (@jsngr) July 25, 2020
A GPT-3 × Figma plugin that takes a URL and a description to mock up a website for you. pic.twitter.com/UsJz0ClGA7
After many hours of retraining my brain to operate in this "priming" approach, I also now have a sick GPT-3 demo: English to LaTeX equations! I'm simultaneously impressed by its coherence and amused by its brittleness -- watch me test the fundamental theorem of calculus.— Shreya Shankar (@sh_reya) July 19, 2020
cc @gdb pic.twitter.com/0dujGOKaYM
Since getting academic access, I’ve been thinking about GPT-3’s applications to grounded language understanding — e.g. for robotics and other embodied agents.— Siddharth Karamcheti (@siddkaramcheti) July 23, 2020
In doing so, I came up with a new demo:
Objects to Affordances: “what can I do with an object?”
cc @gdb pic.twitter.com/ptRXmy197P
Which then inspired a large amount of discussion and interest.
The press, experts in the field, and the general tech community had widely differing opinions on both GPT-3’s capabilities and its potential implications when extensively adapted. Responses have ranged from heralding the future of human productivity and fear over losing jobs to more measured consideration of GPT-3’s capabilities and limitations.
Coverage from the press has increased since the demos came out:
Compared to some of the press, reactions from experts in machine learning and NLP are largely more tempered and driven by curiosity, focusing on issues with how GPT-3 will be deployed and questioning its ability to truly understand language.
Following several conversations, I want to clarify:— (((ل()(ل() 'yoav)))) (@yoavgo) July 18, 2020
I am amazed by its ability to learn various patterns of operation from 1-3 examples and carry them correctly. This is new and *super* exciting.
It does not "master all language" or "understands text".
It is not sentient. https://t.co/WEkxxkVIJm
Anima Anandkumar, director of AI research at NVIDIA and professor of computing and mathematical sciences at Caltech critiqued the OpenAI team for not paying significant attention to bias in the model, especially considering that GPT-2 exhibited similar issues. This was because it was trained on data from unmoderated sources like Reddit, and thus would “learn” biases that are evident in texts written by humans (although it seems that the model can be primed to reduce gender bias).
#gpt3 is surprising and creative but it’s also unsafe due to harmful biases. Prompted to write tweets from one word - Jews, black, women, holocaust - it came up with these (https://t.co/G5POcerE1h). We need more progress on #ResponsibleAI before putting NLG models in production. pic.twitter.com/FAscgUr5Hh— Jerome Pesenti (@an_open_mind) July 18, 2020
“GPT-3 and the buzz behind it is the beginning of the transition of few-shot learning technology from research to actionable products. But every breakthrough technology comes with a lot of social media buzz that can delude our thinking about the capabilities of such technologies.”
“Algorithms for search, optimization, and learning that were once causing headlines about how humanity was about to be overtaken by machines are now powering our productivity software. And games, phone apps, and cars. Now that the technology works reliably, it's no longer AI (it's also a bit boring).”
Commentators from the tech industry had mixed reactions, and some described the implications a “code-writing AI” would have on the labor market.
“So, as we now consider the automation of the craft itself, AI will expedite our workflow in a way where some of that grunt work will no longer bury us in tedium. This will allow for more creative exploration and imaginative thinking — freeing us to discover new design paradigms. In the case of AI, it’s a matter of harnessing it.”
Sam Altman, CEO of OpenAI, responded to the widespread hype around the model by noting that although it certainly indicates progress in AI, there is still much ground to cover.
The GPT-3 hype is way too much. It’s impressive (thanks for the nice compliments!) but it still has serious weaknesses and sometimes makes very silly mistakes. AI is going to change the world, but GPT-3 is just a very early glimpse. We have a lot still to figure out.— Sam Altman (@sama) July 19, 2020
In summary, many experts demonstrate interesting examples of what seems like linguistic comprehension with GPT-3. The press and tech community both applaud the progress of OpenAI’s work while cautioning that it could lead to massive technological upheavals in the future. However, the CEO of OpenAI agrees with researchers and other tech commentators that although GPT-3 represents immense progress in AI, it does not truly understand language, and that there are serious issues with using the model in the real world, such as bias and training time.
The impressive few-shot learning capabilities of GPT-3 represent a significant advance in AI, and the fact that it was achieved just by scaling up existing approaches was a useful discovery. Still, impressive demonstrations of its capabilities have generated an extraordinarily large amount of hype. Here, we will point out reasons that this hype may need to be tempered. In particular, GPT-3’s ability to perform tasks across many domains has re-introduced worries over AI and job loss--for example, the statement that GPT-3 might not only “replace coders, but entire industries” illustrates the concern. While GPT-3 certainly represents substantial progress for language models, it does not boast true “intelligence” or threaten to completely replace workers.
After all, GPT-3’s core model and training procedure is the same as many previous transformer-based models. Although scaling up has conferred significant performance improvements, GPT-3 retains the following limitations inherent to this architecture and training procedure:
The last three points, and as well as other more technical ones, are in fact noted in the GPT-3 paper’s “Limitations” section.
Although technology similar to GPT-3 has the potential to change the nature of many professions, it does not necessarily mean that those professions will disappear. For one, as we have covered, adoption of new technologies is generally a slow and long process and many AI technologies complement rather than replace human workers. The former is even more likely to be the case, because the model is not perfect. Looking at the example of web development, someone who understands the technical details of the field will still be needed to correct and refine GPT-3’s code.
Computer vision, which had many “breakthrough” moments prior to NLP, created similar panics, like how AI would get rid of certain healthcare professions. However, instead of outright replacing doctors like radiologists, AI would be integrated into their workflow. Stanford radiologist Curtis Langlotz aptly noted, “AI won’t replace radiologists, but radiologists who use AI will replace radiologists who don’t.” A similar trend may persist for GPT-3; at the end of the day it is a model, and models are not perfect.
Beyond these pragmatic considerations, there are also theoretical ones. Before transformers were widely used for NLP, state-of-the-art results were achieved by bidirectional recurrent neural networks7 – which incorporate more words in the sentence when predicting a word’s probability under the model. GPT-3 on the other hand, is able to encode much fewer words in context than its recurrent predecessors.8 A paper by Ke Tran, Christof Monz (University of Amsterdam), and Arianna Bisazza (Leiden University) notes that transformers are much worse at modeling hierarchical structure in natural language when compared to recurrent networks. This is important, because a language’s syntax is described in terms of a hierarchy – words combine to make phrases, which combine to make more complex phrases and sentences. Even if transformers still perform effectively, having some learned notion of hierarchical structure is critical when it comes to developing a holistic model of language. While these issues can be mitigated with further research, they are worth noting in assessing the capabilities of GPT-3.
Some have commented that GPT-3 represents a significant step for AI towards “Artificial General Intelligence,” akin to what humans have. While certainly representative of progress, it is worthwhile to discuss a counterpoint to this sort of hype. Computational linguists Emily Bender (University of Washington) and Alexander Koller (Saarland University) recently proposed a thought experiment called the Octopus Test. In the experiment, two people are living on remote islands and communicate through a cable under the ocean, where an octopus is able to listen in on their conversations, acting as a proxy for language models like GPT-3. Eventually, if the octopus can successfully impersonate one of the people, then it “passes the test.” However, Bender and Koller propose several scenarios in which the octopus will be unable to successfully impersonate a person, such as building tools and self-defense. This is because the models only interface with text, and they have no conception of the real-world grounding that is critical to linguistic understanding.
Improving GPT-3 and its successors would be analogous to making the octopus “better at what it does.” As models like GPT-3 get more sophisticated, they will demonstrate different strengths and weaknesses; but learning only from written text is merely an exercise in reproduction: true “knowledge” or “understanding” comes from the interaction between language, the mind, and the real world, something a model like GPT-3 cannot experience.
In summary, GPT-3 is impressive but also fundamentally limited in many ways. Although it shows how NLP can change the nature of certain professions, GPT-3 is not a suitable replacement for humans in these roles due to its limitations. Additionally, it maintains many of the same theoretical limitations of the transformer architecture, failing to effectively model hierarchical structures and bidirectional contexts. The development of GPT-3 comes as the field of NLP discusses both the distinctions between form and meaning and considers alternative methods of evaluating language models, showing that the largest, most sophisticated models still have a long way to go when it comes to truly understanding natural language.
GPT-3 deserves acclaim for pushing the state of the art of language models and demonstrating what current techniques are capable of. But, we must not leap to conclusions about its far-reaching impacts on jobs and industries, and we must be aware of all the limitations in the model that still need to be addressed.
When a learning algorithm or model has more parameters, it is able to represent more complicated relationships from data than it would be able to otherwise. In the context of GPT-3, this means that it can memorize, or infer relationships between, pieces of information on the internet. ↩
GPT-3 being the largest language model to date refers to the fact that the model has a large number of parameters--the more parameters a model has, the more complex the data it can learn from. Large numbers of parameters are a key aspect of successful deep learning models. ↩
A language model is trained to predict the next word in a text, given preceding context. ↩
GPT-2 and GPT-3 are based on the transformer, a novel architecture that has been responsible for many recent advances in NLP. For a discussion of GPT-2 and GPT-3’s architecture, see this post. For a general introduction to Transformers, see this lecture. ↩
Text from the Internet, including Wikipedia, and data from books digitally available ↩
OpenAI is conducting a private beta with users that are individually vetted in order to decrease chances of misuse. According to their API blog post, OpenAI “will terminate API access for use-cases that cause physical or mental harm to people.” ↩
Recurrent neural networks were the first type of neural language model. They are particularly useful for modeling sequential data like sentences because they encode previous information while predicting the current state. This makes them especially effective compared to previously used statistical language models which are primarily count-based. Bidirectional RNNs achieve better results because they incorporate information about the entire sentence, not just a word’s preceding context. ↩
Attention is a method used by transformers, mentioned here. For a fairly technical introduction to bidirectional recurrent neural networks, see here. ↩