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GPT-3

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See also:Generative pre-trained transformer § Foundational models

Generative Pre-trained Transformer 3 (GPT-3)
Original author(s)OpenAI[1]
Initial releaseMay 28, 2020 (publication); June 11, 2020 (OA API beta)
Repository
PredecessorGPT-2
SuccessorGPT-3.5
GPT-4
Type
Websiteopenai.com/blog/openai-api
Part of a series on
Machine learning
anddata mining
Paradigms
Problems
Clustering
Dimensionality reduction
Structured prediction
Anomaly detection
Artificial neural network
Reinforcement learning
Learning with humans
Model diagnostics
Mathematical foundations
Journals and conferences
Related articles

Generative Pre-trained Transformer 3(GPT-3) is alarge language modelreleased byOpenAIin 2020.

Like its predecessor,GPT-2,it is a decoder-only[2]transformer modelof deep neural network, which supersedes recurrence and convolution-based architectures with a technique known as "attention".[3]This attention mechanism allows the model to focus selectively on segments of input text it predicts to be most relevant.[4]GPT-3 has 175 billionparameters,each with 16-bit precision, requiring 350GB of storage since each parameter occupies 2 bytes. It has acontext windowsize of 2048tokens,and has demonstrated strong "zero-shot"and"few-shot"learning abilities on many tasks.[2]

On September 22, 2020,Microsoftannounced that it had licensed GPT-3 exclusively. Others can still receive output from its public API, but only Microsoft has access to the underlying model.[5]

Background

[edit]

According toThe Economist,improved algorithms, more powerful computers, and a recent increase in the amount of digitized material have fueled a revolution inmachine learning.New techniques in the 2010s resulted in "rapid improvements in tasks", including manipulating language.[6]

Software models are trained to learn by using thousands or millions of examples in a "structure... loosely based on the neural architecture of the brain ".[6]One architecture used innatural language processing(NLP) is aneural networkbased on adeep learningmodel that was introduced in 2017—thetransformerarchitecture.[7]There are a number of NLP systems capable of processing, mining, organizing, connecting and contrasting textual input, as well as correctly answering questions.[8]

On June 11, 2018, OpenAI researchers and engineers published a paper introducing the firstgenerative pre-trained transformer(GPT)—a type ofgenerativelarge language modelthat is pre-trained with an enormous and diversetext corpusindatasets,followed by discriminativefine-tuningto focus on a specific task. GPT models are transformer-based deep-learning neural network architectures. Previously, the best-performing neural NLP models commonly employedsupervised learningfrom large amounts of manually-labeled data, which made it prohibitively expensive and time-consuming to train extremely large language models.[2]The first GPT model was known as "GPT-1," and it was followed by "GPT-2" in February 2019. Created as a direct scale-up of its predecessor, GPT-2 had both its parameter count and dataset size increased by a factor of 10. It had 1.5 billion parameters, and was trained on a dataset of 8 million web pages.[9]

In February 2020, Microsoft introduced its Turing Natural Language Generation (T-NLG), which they claimed was "largest language model ever published at 17 billion parameters."[10]It performed better than any other language model at a variety of tasks, includingsummarizing textsandanswering questions.

Training and capabilities

[edit]
A sample student essay aboutpedagogywritten by GPT-3

The construct of "learning styles" is problematic because it fails to account for the processes through which learning styles are shaped. Some students might develop a particular learning style because they have had particular experiences. Others might develop a particular learning style by trying to accommodate to a learning environment that was not well suited to their learning needs. Ultimately, we need to understand the interactions among learning styles and environmental and personal factors, and how these shape how we learn and the kinds of learning we experience.

– Text generated byMike Sharples[11]

On May 28, 2020, anarXivpreprint by a group of 31 engineers and researchers at OpenAI described the achievement and development of GPT-3, a third-generation "state-of-the-art language model".[1][12]The team increased the capacity of GPT-3 by over two orders of magnitude from that of its predecessor, GPT-2,[13]making GPT-3 the largest non-sparse language model to date.[1]: 14 [14]Because GPT-3 is structurally similar to its predecessors,[1]its greater accuracy is attributed to its increased capacity and greater number of parameters.[15]GPT-3's capacity is ten times larger than that of Microsoft's Turing NLG, the next largest NLP model known at the time.[12]

Lambdalabs estimated a hypothetical cost of around $4.6 million US dollars and 355 years to train GPT-3 on a singleGPUin 2020,[16]with lower actual training time by using more GPUs in parallel.

Sixty percent of the weighted pre-training dataset for GPT-3 comes from a filtered version ofCommon Crawlconsisting of 410 billionbyte-pair-encodedtokens. Fuzzy deduplication usedApache Spark'sMinHashLSH.[1]: 9 Other sources are 19 billion tokens from WebText2 representing 22% of the weighted total, 12 billion tokens from Books1 representing 8%, 55 billion tokens from Books2 representing 8%, and 3 billion tokens from Wikipedia representing 3%.[1]: 9 GPT-3 was trained on hundreds of billions of words and is also capable of coding inCSS,JSX,andPython,among others.[citation needed]

GPT-3 training data[1]: 9 
Dataset # tokens Proportion
within training
Common Crawl 410 billion 60%
WebText2 19 billion 22%
Books1 12 billion 8%
Books2 55 billion 8%
Wikipedia 3 billion 3%

Since GPT-3's training data was all-encompassing, it does not require further training for distinct language tasks.[citation needed]The training data contains occasional toxic language and GPT-3 occasionally generates toxic language as a result of mimicking its training data. A study from theUniversity of Washingtonfound that GPT-3 produced toxic language at a toxicity level comparable to the similar natural language processing models ofGPT-2and CTRL. OpenAI has implemented several strategies to limit the amount of toxic language generated by GPT-3. As a result, GPT-3 produced less toxic language compared to its predecessor model, GPT-1, although it produced both more generations and a higher toxicity of toxic language compared to CTRL Wiki, a language model trained entirely on Wikipedia data.[17]

On June 11, 2020,OpenAIannounced that users could request access to its user-friendly GPT-3 API—a "machine learning toolset" —to help OpenAI "explore the strengths and limits" of this new technology.[18][19]The invitation described how this API had a general-purpose "text in, text out" interface that can complete almost "any English language task", instead of the usual single use-case.[18]According to one user, who had access to a private early release of the OpenAI GPT-3 API, GPT-3 was "eerily good" at writing "amazingly coherent text" with only a few simple prompts.[20]In an initial experiment 80 US subjects were asked to judge if short ~200 word articles were written by humans or GPT-3. The participants judged correctly 52% of the time, doing only slightly better than random guessing.[1]

On November 18, 2021, OpenAI announced that enough safeguards had been implemented that access to its API would be unrestricted.[21]OpenAI provided developers with a content moderation tool that helps them abide by OpenAI's content policy.[22]On January 27, 2022, OpenAI announced that its newest GPT-3 language models (collectively referred to as InstructGPT) were now the default language model used on theirAPI.According to OpenAI, InstructGPT produced content that was better aligned to user intentions by following instructions better, generating fewer made-up facts, and producing somewhat less toxic content.[23]

Because GPT-3 can "generate news articles which human evaluators have difficulty distinguishing from articles written by humans,"[12]GPT-3 has the "potential to advance both the beneficial and harmful applications of language models."[1]: 34 In their May 28, 2020 paper, the researchers described in detail the potential "harmful effects of GPT-3"[12]which include "misinformation,spam,phishing,abuse of legal and governmental processes,fraudulent academic essay writingand social engineeringpretexting".[1]The authors draw attention to these dangers to call for research onrisk mitigation.[1]: 34 

GPT-3 is capable of performing zero-shot and few-shot learning (including one-shot).[1]

In June 2022, Almira Osmanovic Thunström wrote that GPT-3 was the primary author on an article on itself, that they had submitted it for publication,[24]and that it had been pre-published while waiting for completion of its review.[25]

GPT-3 models

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There are many models in the GPT-3 family, some serving different purposes than others. In the initial research paper published by OpenAI, they mentioned 8 different sizes of the main GPT-3 model:

Model name Parameters API name
GPT-3 Small 125 M n/a
GPT-3 Medium 350 M ada
GPT-3 Large 760 M n/a
GPT-3 XL 1.3 B babbage
GPT-3 2.7B 2.7 B n/a
GPT-3 6.7B 6.7 B curie
GPT-3 13B 13B n/a
GPT-3 175B 175B davinci

Half of the models are accessible through the API, namely GPT-3-medium, GPT-3-xl, GPT-3-6.7B and GPT-3-175b, which are referred to as ada, babbage, curie and davinci respectively. While the size of the API models was not originally disclosed by OpenAI,EleutherAIannounced the mapping between model sizes and API names in May 2021.[26]These model sizes were later confirmed by OpenAI,[27]but the sizes of subsequent models have not been disclosed.

Model Parameters Description Series
ada 350 M Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost. Base GPT-3
babbage

babbage-002

1.3 B Capable of straightforward tasks, very fast, and lower cost. Base GPT-3
curie 6.7B Very capable, but faster and lower cost than Davinci. Base GPT-3
davinci

davinci-002

175 B Most capable GPT-3 model. Can do any task the other models can do, often with higher quality. Base GPT-3
text-ada-001 350 M Capable of very simple tasks, usually the fastest model in the GPT-3 series, and lowest cost. InstructGPT
text-babbage-001 1.3B Capable of straightforward tasks, very fast, and lower cost. InstructGPT
text-curie-001 6.7B Very capable, faster and lower cost than Davinci. InstructGPT
text-davinci-001 175B Older version of the most capable model in the GPT-3 series. Can perform any task the other GPT-3 models can, often with less context. InstructGPT
text-davinci-002

code-davinci-002

Undisclosed Similar capabilities totext-davinci-003but trained with supervised fine-tuning instead of reinforcement learning GPT-3.5
text-davinci-003 Undisclosed Can do any language task with better quality, longer output, and consistent instruction-following than the curie, babbage, or ada models. Also supports inserting completions within text. GPT-3.5
gpt-3.5-turbo

gpt-3.5-turbo-instruct gpt-3.5-turbo-16k

Undisclosed Most capable and cost effective (fastest) GPT-3.5 model and optimized for chat at 1/10th the cost oftext-davinci-003. GPT-3.5

GPT-3.5

[edit]
Generative Pre-trained Transformer 3.5 (GPT-3.5)
Original author(s)OpenAI[1]
Initial releaseMarch 15, 2022;2 years ago(2022-03-15)
Repositoryn/a
PredecessorGPT-3
SuccessorGPT-4
Type
LicensePrivative
Websiten/a

Generative Pre-trained Transformer 3.5(GPT-3.5) is a sub class of GPT-3 Models created byOpenAIin 2022.

On March 15, 2022, OpenAI made available new versions of GPT-3 andCodexin its API with edit and insert capabilities under the names "text-davinci-002" and "code-davinci-002".[28]These models were described as more capable than previous versions and were trained on data up to June 2021.[29]On November 28, 2022, OpenAI introduced text-davinci-003.[30]On November 30, 2022, OpenAI began referring to these models as belonging to the "GPT-3.5" series,[29]and releasedChatGPT,which wasfine-tunedfrom a model in the GPT-3.5 series.[31]OpenAI does not include GPT-3.5 in GPT-3.[32]

Models

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There are three models:[33]

  • Chat
    • gpt-3.5-turbo
  • Text completion
    • text-davinci-003
    • text-davinci-002

GPT-3.5 with browsing

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On April 10, 2023,OpenAIintroduced a new variant of its GPT-3.5 series model, known as GPT-3.5 with Browsing (ALPHA).[34]This updated model was described to build upon the capabilities of its predecessors "text-davinci-002" and "code-davinci-002".[35]The GPT-3.5 with Browsing (ALPHA) model incorporated the ability to access and browse online information. This has led to more accurate and up-to-date responses to user queries.[34]

The GPT-3.5 with Browsing (ALPHA) model has been trained on data up to September 2021, giving it more information compared to previous GPT-3.5 models, which were trained on data up until June 2021. The model attempted to provide developers and users with an advanced natural language processing tool that can effectively retrieve and synthesize online information.[34]

To enable browsing capabilities, OpenAI implemented a newAPIthat allows the GPT-3.5 with Browsing (ALPHA) model to access selected online resources during operation.[36]This feature allows users to ask questions or request information with the expectation that the model will deliver updated, accurate, and relevant answers based on the latest online sources available to it.

On April 27, 2023, OpenAI made the GPT-3.5 with Browsing (ALPHA) model publicly available to GPT Plus users. This allowed more people to access to its new features.[36]

InstructGPT

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InstructGPT is a fine-tuned version of GPT-3.5 trained on a dataset of human-written instructions.[37]

Reception

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Applications

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  • GPT-3, specifically theCodex model,is the basis forGitHub Copilot,a code completion and generation software that can be used in various code editors and IDEs.[38][39]
  • GPT-3 is used in certainMicrosoftproducts to translate conventional language into formal computer code.[40][41]
  • GPT-3 has been used in CodexDB[42]to generate query-specific code forSQLprocessing.
  • GPT-3 has been used byJason Rohrerin a retro-themed chatbot project named "Project December", which is accessible online and allows users to converse with several AIs using GPT-3 technology.[43]
  • GPT-3 was used byThe Guardianto write an article about AI being harmless to human beings. It was fed some ideas and produced eight different essays, which were ultimately merged into one article.[44]
  • GPT-3 was used inAI Dungeon,which generates text-based adventure games. Later it was replaced by a competing model after OpenAI changed their policy regarding generated content.[45][46]
  • GPT-3 is used to aid in writingcopyand other marketing materials.[47]
  • A 2022 study fromDrexel Universitysuggested that GPT-3-based systems could be used to screen for early signs ofAlzheimer's disease.[48][49]

Reviews

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  • In a July 2020 review inThe New York Times,Farhad Manjoosaid that GPT-3's ability to generate computer code, poetry, and prose is not just "amazing", "spooky", and "humbling", but also "more than a little terrifying".[50]
  • Daily Nouspresented a series of articles by nine philosophers on GPT-3.[51]Australian philosopherDavid Chalmersdescribed GPT-3 as "one of the most interesting and important AI systems ever produced".[52]
  • A review inWiredsaid that GPT-3 was "provoking chills acrossSilicon Valley".[53]
  • TheNational Law Reviewsaid that GPT-3 is an "impressive step in the larger process", with OpenAI and others finding "useful applications for all of this power" while continuing to "work toward a moregeneral intelligence".[54]
  • An article in theMIT Technology Review,co-written by Deep Learning criticGary Marcus,[55]stated that GPT-3's "comprehension of the world is often seriously off, which means you can never really trust what it says."[56]According to the authors, GPT-3 models relationships between words without having anunderstandingof the meaning behind each word.
  • Jerome Pesenti, head of the Facebook AI lab, said GPT-3 is "unsafe," pointing to thesexist,racistand other biased and negative language generated by the system when it was asked to discuss Jews, women, black people, and theHolocaust.[57]
  • Nabla, a French start-up specializing in healthcare technology, tested GPT-3 as a medicalchatbot,though OpenAI itself warned against such use. As expected, GPT-3 showed several limitations. For example, while testing GPT-3 responses about mental health issues, the AI advised a simulated patient to commit suicide.[58]
  • Noam Chomskyexpressed his skepticism about GPT-3's scientific value: "It's not a language model. It works just as well for impossible languages as for actual languages. It is therefore refuted, if intended as a language model, by normal scientific criteria. [...] Perhaps it's useful for some purpose, but it seems to tell us nothing about language or cognition generally."[59]
  • Luciano FloridiandMassimo Chiriattihighlighted the risk of "cheap production of good, semantic artefacts".[60]
  • OpenAI's Sam Altman himself criticized what he called "GPT-3 hype", acknowledging GPT-3 "has serious weakness and sometimes makes very silly mistakes... AI is going to change the world, but GPT-3 is just a very early glimpse."[61]

Criticism

[edit]

GPT-3's builder,OpenAI,was initially founded as anon-profitin 2015.[62]In 2019, OpenAI broke from its usual open-source standards by not publicly releasing GPT-3's predecessor model, citing concerns that the model could facilitate the propagation of fake news. OpenAI eventually released a version ofGPT-2that was 8% of the original model's size.[63]In the same year, OpenAI restructured to be a for-profit company.[64]In 2020, Microsoft announced the company had exclusive licensing of GPT-3 for Microsoft's products and services following a multi-billion dollar investment in OpenAI. The agreement permits OpenAI to offer a public-facing API such that users can send text to GPT-3 to receive the model's output, but only Microsoft will have access to GPT-3's source code.[5]

Large language models, such as GPT-3, have come under criticism from a few of Google's AI ethics researchers for the environmental impact of training and storing the models, detailed in a paper co-authored byTimnit GebruandEmily M. Benderin 2021.[65]

The growing[when?]use of automated writing technologies based on GPT-3 and other language generators, has raised concerns regarding academic integrity[66]and raised the stakes of how universities and schools will gauge what constitutes academic misconduct such as plagiarism.[67]

OpenAI's GPT series was built with data from theCommon Crawldataset,[68]a conglomerate of copyrighted articles, internet posts, web pages, and books scraped from 60 million domains over a period of 12 years.TechCrunchreports this training data includes copyrighted material from the BBC,The New York Times,Reddit,the full text of online books, and more.[69]In its response to a 2019 Request for Comments on Intellectual Property Protection for Artificial Intelligence Innovation from theUnited States Patent and Trademark Office(USPTO), OpenAI argued that "Under current law, training AI systems [such as its GPT models] constitutesfair use,"but that" given the lack ofcase lawon point, OpenAI and other AI developers like us face substantial legal uncertainty and compliance costs. "[70]

See also

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