Jump to content

Generative pre-trained transformer

From Wikipedia, the free encyclopedia
(Redirected from GPT Foundational models)
Not to be confused with ChatGPT.
Part of a series on
Machine learning
and data 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

Original GPT model

A generative pre-trained transformer (GPT) is a type of large language model (LLM)[1][2][3] and a prominent framework for generative artificial intelligence.[4][5] It is an artificial neural network that is used in natural language processing by machines.[6] It is based on the transformer deep learning architecture, pre-trained on large data sets of unlabeled text, and able to generate novel human-like content.[2][3] As of 2023, most LLMs had these characteristics[7] and are sometimes referred to broadly as GPTs.[8]

The first GPT was introduced in 2018 by OpenAI.[9] OpenAI has released significant GPT foundation models that have been sequentially numbered, to comprise its "GPT-n" series.[10] Each of these was significantly more capable than the previous, due to increased size (number of trainable parameters) and training. The most recent of these, GPT-4o, was released in May 2024.[11] Such models have been the basis for their more task-specific GPT systems, including models fine-tuned for instruction following—which in turn power the ChatGPT chatbot service.[1]

The term "GPT" is also used in the names and descriptions of such models developed by others. For example, other GPT foundation models include a series of models created by EleutherAI,[12] and seven models created by Cerebras in 2023.[13] Companies in different industries have developed task-specific GPTs in their respective fields, such as Salesforce's "EinsteinGPT" (for CRM)[14] and Bloomberg's "BloombergGPT" (for finance).[15]

History

[edit]

Initial developments

[edit]

Generative pretraining (GP) was a long-established concept in machine learning applications.[16][17] It was originally used as a form of semi-supervised learning, as the model is trained first on an unlabeled dataset (pretraining step) by learning to generate datapoints in the dataset, and then it is trained to classify a labeled dataset.[18]

There were three main types of early GP. The hidden Markov models learn a generative model of sequences for downstream applications. For example, in speech recognition, a trained HMM infers the most likely hidden sequence for a speech signal, and the hidden sequence is taken as the phonemes of the speech signal. These were developed in the 1970s and became widely applied in speech recognition in the 1980s.[19][20]

The compressors learn to compress data such as images and textual sequences, and the compressed data serves as a good representation for downstream applications such as facial recognition.[21][22][23] The autoencoders similarly learn a latent representation of data for later downstream applications such as speech recognition.[24][25] The connection between autoencoders and algorithmic compressors was noted in 1993.[26]

See also: Transformer (deep learning architecture) § History

During the 2010s, the problem of machine translation was solved[citation needed] by recurrent neural networks, with attention mechanism added. This was optimized into the transformer architecture, published by Google researchers in Attention Is All You Need (2017).[27] That development led to the emergence of large language models such as BERT (2018)[28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series.[29]

Previously in 2017, some of the authors who would later work on GPT-1 worked on generative pre-training of language with LSTM, which resulted in a model that could represent text with vectors that could easily be fine-tuned for downstream applications.[30]

Prior to transformer-based architectures, the best-performing neural NLP (natural language processing) models commonly employed supervised learning from large amounts of manually-labeled data. The reliance on supervised learning limited their use on datasets that were not well-annotated, and also made it prohibitively expensive and time-consuming to train extremely large language models.[29]

The semi-supervised approach OpenAI employed to make a large-scale generative system—and was first to do with a transformer model—involved two stages: an unsupervised generative "pretraining" stage to set initial parameters using a language modeling objective, and a supervised discriminative "fine-tuning" stage to adapt these parameters to a target task.[29]

Later developments

[edit]

Regarding more recent GPT foundation models, OpenAI published its first versions of GPT-3 in July 2020. There were three models, with 1B, 6.7B, 175B parameters, respectively named babbage, curie, and davinci (giving initials B, C, and D).[citation needed]

In July 2021, OpenAI published Codex, a task-specific GPT model targeted for programming applications. This was developed by fine-tuning a 12B parameter version of GPT-3 (different from previous GPT-3 models) using code from GitHub.[31]

In March 2022, OpenAI published two versions of GPT-3 that were fine-tuned for instruction-following (instruction-tuned), named davinci-instruct-beta (175B) and text-davinci-001,[32] and then started beta testing code-davinci-002.[33] text-davinci-002 was instruction-tuned from code-davinci-002. Both text-davinci-003 and ChatGPT were released in November 2022, with both building upon text-davinci-002 via reinforcement learning from human feedback (RLHF). text-davinci-003 is trained for following instructions (like its predecessors), whereas ChatGPT is further trained for conversational interaction with a human user.[34][35]

OpenAI's most recent GPT foundation model, GPT-4, was released on March 14, 2023. It can be accessed directly by users via a premium version of ChatGPT, and is available to developers for incorporation into other products and services via OpenAI's API. Other producers of GPT foundation models include EleutherAI (with a series of models starting in March 2021)[12] and Cerebras (with seven models released in March 2023).[13]

Foundation models

[edit]

A foundation model is an AI model trained on broad data at scale such that it can be adapted to a wide range of downstream tasks.[36][37]

Thus far, the most notable GPT foundation models have been from OpenAI's GPT-n series. The most recent from that is GPT-4, for which OpenAI declined to publish the size or training details (citing "the competitive landscape and the safety implications of large-scale models").[38]

OpenAI's GPT-n series
Model Architecture Parameter count Training data Release date Training cost
GPT-1 12-level, 12-headed Transformer decoder (no encoder), followed by linear-softmax. 117 million BookCorpus:[39] 4.5 GB of text, from 7,000 unpublished books of various genres. June 11, 2018[9] 30 days on 8 P600 graphics cards, or 1 petaFLOPS-day.[9]
GPT-2 GPT-1, but with modified normalization 1.5 billion WebText: 40 GB of text, 8 million documents, from 45 million webpages upvoted on Reddit. February 14, 2019 (initial/limited version) and November 5, 2019 (full version)[40] "tens of petaflop/s-day",[41] or 1.5e21 FLOPS.[42]
GPT-3 GPT-2, but with modification to allow larger scaling 175 billion[43] 499 billion tokens consisting of CommonCrawl (570 GB), WebText, English Wikipedia, and two books corpora (Books1 and Books2). May 28, 2020[41] 3640 petaflop/s-day (Table D.1[41]), or 3.1e23 FLOPS.[42]
GPT-3.5 Undisclosed 175 billion[43] Undisclosed March 15, 2022 Undisclosed
GPT-4 Also trained with both text prediction and RLHF; accepts both text and images as input. Further details are not public.[38] Undisclosed. Estimated 1.7 trillion.[44] Undisclosed March 14, 2023 Undisclosed. Estimated 2.1 × 1025 FLOPS.[42]
GPT-4o ? ? ? May 2024 ?
GPT-4.5 ? ? ? February 2025 ?

Other such models include Google's PaLM, a broad foundation model that has been compared to GPT-3 and has been made available to developers via an API,[45][46] and Together's GPT-JT, which has been reported as the closest-performing open-source alternative to GPT-3 (and is derived from earlier open-source GPTs).[47] Meta AI (formerly Facebook) also has a generative transformer-based foundational large language model, known as LLaMA.[48]

Foundational GPTs can also employ modalities other than text, for input and/or output. GPT-4 is a multi-modal LLM that is capable of processing text and image input (though its output is limited to text).[49] Regarding multimodal output, some generative transformer-based models are used for text-to-image technologies such as diffusion[50] and parallel decoding.[51] Such kinds of models can serve as visual foundation models (VFMs) for developing downstream systems that can work with images.[52]

Task-specific models

[edit]

A foundational GPT model can be further adapted to produce more targeted systems directed to specific tasks and/or subject-matter domains. Methods for such adaptation can include additional fine-tuning (beyond that done for the foundation model) as well as certain forms of prompt engineering.[53]

An important example of this is fine-tuning models to follow instructions, which is of course a fairly broad task but more targeted than a foundation model. In January 2022, OpenAI introduced "InstructGPT"—a series of models which were fine-tuned to follow instructions using a combination of supervised training and reinforcement learning from human feedback (RLHF) on base GPT-3 language models.[54][55] Advantages this had over the bare foundational models included higher accuracy, less negative/toxic sentiment, and generally better alignment with user needs. Hence, OpenAI began using this as the basis for its API service offerings.[56] Other instruction-tuned models have been released by others, including a fully open version.[57][58]

Another (related) kind of task-specific models are chatbots, which engage in human-like conversation. In November 2022, OpenAI launched ChatGPT—an online chat interface powered by an instruction-tuned language model trained in a similar fashion to InstructGPT.[59] They trained this model using RLHF, with human AI trainers providing conversations in which they played both the user and the AI, and mixed this new dialogue dataset with the InstructGPT dataset for a conversational format suitable for a chatbot. Other major chatbots currently include Microsoft's Bing Chat, which uses OpenAI's GPT-4 (as part of a broader close collaboration between OpenAI and Microsoft),[60] and Google's competing chatbot Gemini (initially based on their LaMDA family of conversation-trained language models, with plans to switch to PaLM).[61]

Yet another kind of task that a GPT can be used for is the meta-task of generating its own instructions, like developing a series of prompts for 'itself' to be able to effectuate a more general goal given by a human user.[62] This is known as an AI agent, and more specifically a recursive one because it uses results from its previous self-instructions to help it form its subsequent prompts; the first major example of this was Auto-GPT (which uses OpenAI's GPT models), and others have since been developed as well.[63]

Multimodality

[edit]

Generative transformer-based systems can also be targeted for tasks involving modalities beyond text. For example, Microsoft's "Visual ChatGPT" combines ChatGPT with visual foundation models (VFMs) to enable input or output comprising images as well as text.[64] Also, advances in text-to-speech technology offer tools for audio content creation when used in conjunction with foundational GPT language models.[65]

Domain-specificity

[edit]

GPT systems can be directed toward particular fields or domains. Some reported examples of such models and apps are as follows:

  • EinsteinGPT – for sales and marketing domains, to aid with customer relationship management (uses GPT-3.5)[66][67]
  • BloombergGPT – for the financial domain, to aid with financial news and information (uses "freely available" AI methods, combined with their proprietary data)[68]
  • Khanmigo – described as a GPT version for tutoring, in the education domain, it aids students using Khan Academy by guiding them through their studies without directly providing answers (powered by GPT-4)[69][70]
  • SlackGPT – for the Slack instant-messaging service, to aid with navigating and summarizing discussions on it (uses OpenAI's API)[71]
  • BioGPT – for the biomedical domain, to aid with biomedical literature text generation and mining (uses GPT-2)[72]

Sometimes domain-specificity is accomplished via software plug-ins or add-ons. For example, several different companies have developed particular plugins that interact directly with OpenAI's ChatGPT interface,[73][74] and Google Workspace has available add-ons such as "GPT for Sheets and Docs"—which is reported to aid use of spreadsheet functionality in Google Sheets.[75][76]

In November 2023, OpenAI announced that ChatGPT Plus subscribers would be able to create custom versions of ChatGPT (being called GPTs).[77] These can be tailored for specific domains via prompt engineering, curated datasets, and/or targeted interaction with external tools. Users who register as verified builders are able to publish their custom GPTs for other users, with monetization potential. (This is notably distinct from OpenAI's API service, as this is based internally within OpenAI's platform.)

Brand issues

[edit]

OpenAI, which created the first generative pre-trained transformer (GPT) in 2018, asserted in 2023 that "GPT" should be regarded as a brand of OpenAI.[78] In April 2023, OpenAI revised the brand guidelines in its terms of service to indicate that other businesses using its API to run their artificial intelligence (AI) services would no longer be able to include "GPT" in such names or branding.[79] In May 2023, OpenAI engaged a brand management service to notify its API customers of this policy, although these notifications stopped short of making overt legal claims (such as allegations of trademark infringement or demands to cease and desist).[78] As of November 2023, OpenAI still prohibits its API licensees from naming their own products with "GPT",[80] but it has begun enabling its ChatGPT Plus subscribers to make "custom versions of ChatGPT" that are being called GPTs on the OpenAI site.[81] OpenAI's terms of service says that its subscribers may use "GPT" in the names of these, although it's "discouraged".[80]

Relatedly, OpenAI has applied to the United States Patent and Trademark Office (USPTO) to seek domestic trademark registration for the term "GPT" in the field of AI.[78] OpenAI sought to expedite handling of its application, but the USPTO declined that request in April 2023.[82] In May 2023, the USPTO responded to the application with a determination that "GPT" was both descriptive and generic.[83] As of November 2023, OpenAI continues to pursue its argument through the available processes. Regardless, failure to obtain a registered U.S. trademark does not preclude some level of common-law trademark rights in the U.S.,[84] and/or trademark rights in other countries.[85]

For any given type or scope of trademark protection in the U.S., OpenAI would need to establish that the term is actually "distinctive" to their specific offerings in addition to being a broader technical term for the kind of technology. Some media reports suggested that OpenAI may be able to obtain trademark registration based indirectly on the fame of its GPT-based chatbot product, ChatGPT,[82][86] for which OpenAI has separately sought protection (and which it has sought to enforce more strongly).[87] Other reports have indicated that registration for the bare term "GPT" seems unlikely to be granted,[78][88] as it is used frequently as a common term to refer simply to AI systems that involve generative pre-trained transformers.[3][89][90][91] In any event, to whatever extent exclusive rights in the term may occur the U.S., others would need to avoid using it for similar products or services in ways likely to cause confusion.[88][92] If such rights ever became broad enough to implicate other well-established uses in the field, the trademark doctrine of descriptive fair use could still continue non-brand-related usage.[93]

Selected bibliography

[edit]

This section lists the main official publications from OpenAI and Microsoft on their GPT models.

  • GPT-1: report,[9] GitHub release.[94]
  • GPT-2: blog announcement,[95] report on its decision of "staged release",[96] GitHub release.[97]
  • GPT-3: report.[41] No GitHub or any other form of code release thenceforth.
  • WebGPT: blog announcement,[98] report,[99]
  • InstructGPT: blog announcement,[54] report.[55]
  • ChatGPT: blog announcement (no report).[59]
  • GPT-4: blog announcement,[100] reports,[101][102] model card.[103]
  • GPT-4o: blog announcement.[104]
  • GPT-4.5: blog announcement.[105]

See also

[edit]

References

[edit]
  1. ^ a b Haddad, Mohammed. "How does GPT-4 work and how can you start using it in ChatGPT?". www.aljazeera.com. Archived from the original on July 5, 2023. Retrieved April 10, 2023.
  2. ^ a b "Generative AI: a game-changer society needs to be ready for". World Economic Forum. January 9, 2023. Archived from the original on April 25, 2023. Retrieved April 8, 2023.
  3. ^ a b c "The A to Z of Artificial Intelligence". Time. April 13, 2023. Archived from the original on June 16, 2023. Retrieved April 14, 2023.
  4. ^ Hu, Luhui (November 15, 2022). "Generative AI and Future". Medium. Archived from the original on June 5, 2023. Retrieved April 29, 2023.
  5. ^ "CSDL | IEEE Computer Society". www.computer.org. Archived from the original on April 28, 2023. Retrieved April 29, 2023.
  6. ^ "LibGuides: Using AI Language Models : ChatGPT". Archived from the original on December 8, 2023. Retrieved December 7, 2023.
  7. ^ Toews, Rob. "The Next Generation Of Large Language Models". Forbes. Archived from the original on April 14, 2023. Retrieved April 9, 2023.
  8. ^ Mckendrick, Joe (March 13, 2023). "Most Jobs Soon To Be 'Influenced' By Artificial Intelligence, Research Out Of OpenAI And University Of Pennsylvania Suggests". Forbes. Archived from the original on April 16, 2023. Retrieved April 16, 2023.
  9. ^ a b c d "Improving language understanding with unsupervised learning". openai.com. June 11, 2018. Archived from the original on March 18, 2023. Retrieved March 18, 2023.
  10. ^ "GPT-1 to GPT-4: Each of OpenAI's GPT Models Explained and Compared". MUO. April 11, 2023. Archived from the original on April 15, 2023. Retrieved May 3, 2023.
  11. ^ "GPT-4". openai.com. Archived from the original on March 14, 2023. Retrieved December 8, 2023.
  12. ^ a b Alford, Anthony (July 13, 2021). "EleutherAI Open-Sources Six Billion Parameter GPT-3 Clone GPT-J". InfoQ. Archived from the original on February 10, 2023. Retrieved April 3, 2023.
  13. ^ a b "News" (Press release). Archived from the original on April 5, 2023. Retrieved April 5, 2023.
  14. ^ Morrison, Ryan (March 7, 2023). "Salesforce launches EinsteinGPT built with OpenAI technology". Tech Monitor. Archived from the original on April 15, 2023. Retrieved April 10, 2023.
  15. ^ "The ChatGPT of Finance is Here, Bloomberg is Combining AI and Fintech". Forbes. Archived from the original on April 6, 2023. Retrieved April 6, 2023.
  16. ^ Hinton (et-al), Geoffrey (October 15, 2012). "Deep neural networks for acoustic modeling in speech recognition" (PDF). IEEE Signal Processing Magazine. Digital Object Identifier 10.1109/MSP.2012.2205597. doi:10.1109/MSP.2012.2205597. S2CID 206485943. Archived (PDF) from the original on March 18, 2023. Retrieved April 27, 2023.
  17. ^ Deng, Li (January 22, 2014). "A tutorial survey of architectures, algorithms, and applications for deep learning | APSIPA Transactions on Signal and Information Processing | Cambridge Core". Apsipa Transactions on Signal and Information Processing. 3. Cambridge.org: e2. doi:10.1017/atsip.2013.9. S2CID 9928823.
  18. ^ Erhan, Dumitru; Courville, Aaron; Bengio, Yoshua; Vincent, Pascal (March 31, 2010). "Why Does Unsupervised Pre-training Help Deep Learning?". Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. JMLR Workshop and Conference Proceedings: 201–208. Archived from the original on January 24, 2024. Retrieved January 24, 2024.
  19. ^ "First-Hand:The Hidden Markov Model – Engineering and Technology History Wiki". ethw.org. January 12, 2015. Archived from the original on April 3, 2018. Retrieved May 1, 2018.
  20. ^ Juang, B. H.; Rabiner, L. R. (1991). "Hidden Markov Models for Speech Recognition". Technometrics. 33 (3): 251–272. doi:10.2307/1268779. ISSN 0040-1706. JSTOR 1268779. Archived from the original on October 8, 2024. Retrieved October 4, 2024.
  21. ^ Cottrell, Garrison W.; Munro, Paul; Zipser, David (1987). "Learning Internal Representation From Gray-Scale Images: An Example of Extensional Programming". Proceedings of the Annual Meeting of the Cognitive Science Society. 9. Archived from the original on October 7, 2024. Retrieved October 4, 2024.
  22. ^ Cottrell, Garrison W. (January 1, 1991), Touretzky, David S.; Elman, Jeffrey L.; Sejnowski, Terrence J.; Hinton, Geoffrey E. (eds.), "Extracting features from faces using compression networks: Face, identity, emotion, and gender recognition using holons", Connectionist Models, Morgan Kaufmann, pp. 328–337, ISBN 978-1-4832-1448-1, archived from the original on October 7, 2024, retrieved October 4, 2024
  23. ^ Schmidhuber, Jürgen (1992). "Learning complex, extended sequences using the principle of history compression" (PDF). Neural Computation. 4 (2): 234–242. doi:10.1162/neco.1992.4.2.234. S2CID 18271205.
  24. ^ Elman, Jeffrey L.; Zipser, David (April 1, 1988). "Learning the hidden structure of speech". The Journal of the Acoustical Society of America. 83 (4): 1615–1626. Bibcode:1988ASAJ...83.1615E. doi:10.1121/1.395916. ISSN 0001-4966. PMID 3372872. Archived from the original on October 7, 2024. Retrieved October 4, 2024.
  25. ^ Bourlard, H.; Kamp, Y. (1988). "Auto-association by multilayer perceptrons and singular value decomposition". Biological Cybernetics. 59 (4–5): 291–294. doi:10.1007/BF00332918. PMID 3196773. S2CID 206775335. Archived from the original on June 27, 2021. Retrieved October 4, 2024.
  26. ^ Hinton, Geoffrey E; Zemel, Richard (1993). "Autoencoders, Minimum Description Length and Helmholtz Free Energy". Advances in Neural Information Processing Systems. 6. Morgan-Kaufmann. Archived from the original on August 14, 2024. Retrieved October 4, 2024.
  27. ^ Vaswani, Ashish; Shazeer, Noam; Parmar, Niki; Uszkoreit, Jakob; Jones, Llion; Gomez, Aidan N; Kaiser, Łukasz; Polosukhin, Illia (2017). "Attention is All you Need" (PDF). Advances in Neural Information Processing Systems. 30. Curran Associates, Inc. Archived (PDF) from the original on February 21, 2024. Retrieved January 28, 2024.
  28. ^ Devlin, Jacob; Chang, Ming-Wei; Lee, Kenton; Toutanova, Kristina (May 24, 2019). "BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding". Association for Computational Linguistics. arXiv:1810.04805.
  29. ^ a b c Radford, Alec; Narasimhan, Karthik; Salimans, Tim; Sutskever, Ilya (June 11, 2018). "Improving Language Understanding by Generative Pre-Training" (PDF). OpenAI. p. 12. Archived (PDF) from the original on January 26, 2021. Retrieved January 23, 2021.
  30. ^ Radford, Alec; Jozefowicz, Rafal; Sutskever, Ilya (April 6, 2017). "Learning to Generate Reviews and Discovering Sentiment". arXiv:1704.01444 [cs.LG].
  31. ^ Chen, Mark; Tworek, Jerry; Jun, Heewoo; Yuan, Qiming; Ponde de Oliveira Pinto, Henrique; Kaplan, Jared; Edwards, Harri; Burda, Yuri; Joseph, Nicholas; Brockman, Greg; Ray, Alex; Puri, Raul; Krueger, Gretchen; Petrov, Michael; Khlaaf, Heidy (July 1, 2021). "Evaluating Large Language Models Trained on Code". Association for Computational Linguistics. arXiv:2107.03374.
  32. ^ Ouyang, Long; Wu, Jeffrey; Jiang, Xu; Almeida, Diogo; Wainwright, Carroll; Mishkin, Pamela; Zhang, Chong; Agarwal, Sandhini; Slama, Katarina; Ray, Alex; Schulman, John; Hilton, Jacob; Kelton, Fraser; Miller, Luke; Simens, Maddie (December 6, 2022). "Training language models to follow instructions with human feedback". Advances in Neural Information Processing Systems. 35: 27730–27744. arXiv:2203.02155. Archived from the original on June 28, 2023. Retrieved June 24, 2023.
  33. ^ "New GPT-3 capabilities: Edit & insert". openai.com. Archived from the original on June 29, 2023. Retrieved June 24, 2023.
  34. ^ Fu, Yao; Peng, Hao; Khot, Tushar (2022). "How does GPT Obtain its Ability? Tracing Emergent Abilities of Language Models to their Sources". Yao Fu's Notion. Archived from the original on April 19, 2023. Retrieved June 24, 2023.
  35. ^ "Model index for researchers". OpenAI API. Archived from the original on June 23, 2023. Retrieved June 23, 2023.
  36. ^ "Introducing the Center for Research on Foundation Models (CRFM)". Stanford HAI. August 18, 2021. Archived from the original on June 4, 2023. Retrieved April 26, 2023.
  37. ^ "Reflections on Foundation Models". hai.stanford.edu. October 18, 2021. Archived from the original on August 15, 2024. Retrieved August 15, 2024.
  38. ^ a b OpenAI (2023). "GPT-4 Technical Report" (PDF). Archived (PDF) from the original on March 14, 2023. Retrieved March 16, 2023.
  39. ^ Zhu, Yukun; Kiros, Ryan; Zemel, Rich; Salakhutdinov, Ruslan; Urtasun, Raquel; Torralba, Antonio; Fidler, Sanja (2015). Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books. IEEE International Conference on Computer Vision (ICCV) 2015. pp. 19–27. arXiv:1506.06724. Archived from the original on February 5, 2023. Retrieved February 7, 2023.
  40. ^ Vincent, James (November 7, 2019). "OpenAI has published the text-generating AI it said was too dangerous to share". The Verge. Archived from the original on June 11, 2020. Retrieved April 28, 2023.
  41. ^ a b c d Brown, Tom B.; Mann, Benjamin; Ryder, Nick; Subbiah, Melanie; Kaplan, Jared; Dhariwal, Prafulla; Neelakantan, Arvind; Shyam, Pranav; Sastry, Girish; Askell, Amanda; Agarwal, Sandhini; Herbert-Voss, Ariel; Krueger, Gretchen; Henighan, Tom; Child, Rewon; Ramesh, Aditya; Ziegler, Daniel M.; Wu, Jeffrey; Winter, Clemens; Hesse, Christopher; Chen, Mark; Sigler, Eric; Litwin, Mateusz; Gray, Scott; Chess, Benjamin; Clark, Jack; Berner, Christopher; McCandlish, Sam; Radford, Alec; Sutskever, Ilya; Amodei, Dario (May 28, 2020). "Language Models are Few-Shot Learners". NeurIPS. arXiv:2005.14165v4.
  42. ^ a b c "ML input trends visualization". Epoch. Archived from the original on July 16, 2023. Retrieved May 2, 2023.
  43. ^ a b Ver Meer, Dave (June 1, 2023). "ChatGPT Statistics". NamePepper. Archived from the original on June 5, 2023. Retrieved June 9, 2023.
  44. ^ "GPT-4 has more than a trillion parameters – Report". March 25, 2023. Archived from the original on March 4, 2024. Retrieved October 23, 2023.
  45. ^ Vincent, James (March 14, 2023). "Google opens up its AI language model PaLM to challenge OpenAI and GPT-3". The Verge. Archived from the original on March 14, 2023. Retrieved April 29, 2023.
  46. ^ "Google Opens Access to PaLM Language Model". Archived from the original on May 31, 2023. Retrieved April 29, 2023.
  47. ^ Iyer, Aparna (November 30, 2022). "Meet GPT-JT, the Closest Open Source Alternative to GPT-3". Analytics India Magazine. Archived from the original on June 2, 2023. Retrieved April 29, 2023.
  48. ^ "Meta Debuts AI Language Model, But It's Only for Researchers". PCMAG. February 24, 2023. Archived from the original on July 19, 2023. Retrieved May 21, 2023.
  49. ^ Islam, Arham (March 27, 2023). "Multimodal Language Models: The Future of Artificial Intelligence (AI)". Archived from the original on May 15, 2023. Retrieved May 15, 2023.
  50. ^ Islam, Arham (November 14, 2022). "How Do DALL·E 2, Stable Diffusion, and Midjourney Work?". Archived from the original on July 18, 2023. Retrieved May 21, 2023.
  51. ^ Saha, Shritama (January 4, 2023). "Google Launches Muse, A New Text-to-Image Transformer Model". Analytics India Magazine. Archived from the original on May 15, 2023. Retrieved May 15, 2023.
  52. ^ Wu (et-al), Chenfei (March 8, 2023). "Visual ChatGPT". arXiv:2303.04671 [cs.CV].
  53. ^ Bommasani (et-al), Rishi (July 12, 2022). "On the Opportunities and Risks of Foundation Models". arXiv:2108.07258 [cs.LG].
  54. ^ a b "Aligning language models to follow instructions". openai.com. Archived from the original on March 23, 2023. Retrieved March 23, 2023.
  55. ^ a b Ouyang, Long; Wu, Jeff; Jiang, Xu; et al. (November 4, 2022). "Training language models to follow instructions with human feedback". NeurIPS. arXiv:2203.02155.
  56. ^ Ramnani, Meeta (January 28, 2022). "OpenAI dumps its own GPT-3 for something called InstructGPT, and for right reason". Analytics India Magazine. Archived from the original on June 4, 2023. Retrieved April 29, 2023.
  57. ^ "Stanford CRFM". crfm.stanford.edu. Archived from the original on April 6, 2023. Retrieved May 15, 2023.
  58. ^ "Free Dolly: Introducing the World's First Truly Open Instruction-Tuned LLM". Databricks. April 12, 2023. Archived from the original on July 14, 2023. Retrieved May 15, 2023.
  59. ^ a b "Introducing ChatGPT". openai.com. Archived from the original on March 16, 2023. Retrieved March 16, 2023.
  60. ^ Wiggers, Kyle (May 4, 2023). "Microsoft doubles down on AI with new Bing features". Archived from the original on December 7, 2023. Retrieved May 4, 2023.
  61. ^ "ChatGPT vs. Bing vs. Google Bard: Which AI Is the Most Helpful?". CNET. Archived from the original on July 24, 2023. Retrieved April 30, 2023.
  62. ^ "Auto-GPT, BabyAGI, and AgentGPT: How to use AI agents". Mashable. April 19, 2023. Archived from the original on July 22, 2023. Retrieved May 15, 2023.
  63. ^ Marr, Bernard. "Auto-GPT May Be The Strong AI Tool That Surpasses ChatGPT". Forbes. Archived from the original on May 21, 2023. Retrieved May 15, 2023.
  64. ^ "Microsoft Open-Sources Multimodal Chatbot Visual ChatGPT". InfoQ. Archived from the original on June 3, 2023. Retrieved May 15, 2023.
  65. ^ Edwards, Benj (January 9, 2023). "Microsoft's new AI can simulate anyone's voice with 3 seconds of audio". Ars Technica. Archived from the original on July 18, 2023. Retrieved May 15, 2023.
  66. ^ Morrison, Ryan (March 7, 2023). "Salesforce launches EinsteinGPT built with OpenAI technology". Archived from the original on April 15, 2023. Retrieved April 10, 2023.
  67. ^ Sharma, Animesh K.; Sharma, Rahul (2023). "The role of generative pretrained transformers (GPTs) in revolutionising digital marketing: A conceptual model". Journal of Cultural Marketing Strategy. 8 (1): 80–90. doi:10.69554/TLVQ2275.
  68. ^ Leswing, Kif (April 13, 2023). "Bloomberg plans to integrate GPT-style A.I. into its terminal". CNBC. Archived from the original on May 19, 2023. Retrieved May 4, 2023.
  69. ^ Melendez, Steven (May 4, 2023). "Learning nonprofit Khan Academy is piloting a version of GPT called Khanmigo". Fast Company. Archived from the original on May 11, 2023. Retrieved May 22, 2023.
  70. ^ "Khan Academy Pilots GPT-4 Powered Tool Khanmigo for Teachers". THE Journal. Archived from the original on May 7, 2023. Retrieved May 7, 2023.
  71. ^ Hachman, Mark (May 4, 2023). "Slack GPT will bring AI chatbots to your conversations". PCWorld. Archived from the original on June 9, 2023. Retrieved May 4, 2023.
  72. ^ Luo (et-al), Renqian (April 3, 2023). "BioGPT: Generative pre-trained transformer for biomedical text generation and mining". Briefings in Bioinformatics. 23 (6). arXiv:2210.10341. doi:10.1093/bib/bbac409. PMID 36156661.
  73. ^ John, Amy Sarah (May 5, 2023). "Know about ChatGPT's 13 best plugins, designed to improve your overall user experience". Latest Digital Transformation Trends | Cloud News | Wire19. Archived from the original on May 9, 2023. Retrieved May 7, 2023.
  74. ^ "ChatGPT plugins". openai.com. March 13, 2024. Archived from the original on March 23, 2023. Retrieved May 7, 2023.
  75. ^ "How to Use ChatGPT on Google Sheets With GPT for Sheets and Docs". MUO. March 12, 2023. Archived from the original on June 19, 2023. Retrieved May 7, 2023.
  76. ^ Asay, Matt (February 27, 2023). "Embrace and extend Excel for AI data prep". InfoWorld. Archived from the original on June 2, 2023. Retrieved May 7, 2023.
  77. ^ "OpenAI GPTS". November 10, 2023. Archived from the original on December 29, 2023. Retrieved December 29, 2023.
  78. ^ a b c d Hicks, William (May 10, 2023). "ChatGPT creator OpenAI is asking startups to remove 'GPT' from their names". The Business Journal. Archived from the original on June 28, 2023. Retrieved May 21, 2023.
  79. ^ OpenAI (April 24, 2023). "Brand Guidelines". Archived from the original on July 18, 2023. Retrieved May 21, 2023.
  80. ^ a b "Brand guidelines". Archived from the original on July 18, 2023. Retrieved November 28, 2023.
  81. ^ "Introducing GPTS". March 13, 2024. Archived from the original on March 20, 2024. Retrieved November 28, 2023.
  82. ^ a b Heah, Alexa (April 26, 2023). "OpenAI Unsuccessful At Speeding Up Its Attempt To Trademark 'GPT'". DesignTAXI. Archived from the original on April 26, 2023. Retrieved May 21, 2023.
  83. ^ "NONFINAL OFFICE ACTION". USPTO. May 25, 2023. Archived from the original on December 3, 2023. Retrieved December 30, 2023.
  84. ^ "U.S. Trademark Law". December 2015. Archived from the original on January 17, 2024. Retrieved November 29, 2023.
  85. ^ "International Trademark Rights". Archived from the original on March 11, 2024. Retrieved November 29, 2023.
  86. ^ "OpenAI Wants to Trademark 'GPT' Amid Rise of AI Chatbots". Tech Times. April 25, 2023. Archived from the original on April 25, 2023. Retrieved May 21, 2023.
  87. ^ Louise, Nickie (April 3, 2023). "OpenAI files a UDRP case against the current owner of ChatGPT.com". Archived from the original on June 5, 2023. Retrieved May 21, 2023.
  88. ^ a b Demcak, Tramatm-Igor (April 26, 2023). "OpenAI's Battle for Brand Protection: Can GPT be trademarked?". Lexology. Archived from the original on May 5, 2023. Retrieved May 22, 2023.
  89. ^ Lawton, George (April 20, 2023). "ChatGPT vs. GPT: How are they different? | TechTarget". Enterprise AI. Archived from the original on May 9, 2023. Retrieved May 21, 2023.
  90. ^ Robb, Drew (April 12, 2023). "GPT-4 vs. ChatGPT: AI Chatbot Comparison". eWEEK. Archived from the original on July 27, 2023. Retrieved May 21, 2023.
  91. ^ Russo, Philip (August 22, 2023). "The Genesis of Generative AI for Everything Everywhere All at Once in CRE". Commercial Observer. Archived from the original on August 24, 2023.
  92. ^ "Trademark infringement". Archived from the original on November 30, 2023. Retrieved November 29, 2023.
  93. ^ Rheintgen, Husch Blackwell LLP-Kathleen A. (August 16, 2013). "Branding 101: trademark descriptive fair use". Lexology. Archived from the original on May 21, 2023. Retrieved May 21, 2023.
  94. ^ finetune-transformer-lm, OpenAI, June 11, 2018, archived from the original on May 19, 2023, retrieved May 1, 2023
  95. ^ "GPT-2: 1.5B release". openai.com. Archived from the original on March 31, 2023. Retrieved May 1, 2023.
  96. ^ Solaiman, Irene; Brundage, Miles; Clark, Jack; Askell, Amanda; Herbert-Voss, Ariel; Wu, Jeff; Radford, Alec; Krueger, Gretchen; Kim, Jong Wook; Kreps, Sarah; McCain, Miles; Newhouse, Alex; Blazakis, Jason; McGuffie, Kris; Wang, Jasmine (November 12, 2019). "Release Strategies and the Social Impacts of Language Models". arXiv:1908.09203 [cs.CL].
  97. ^ gpt-2, OpenAI, May 1, 2023, archived from the original on March 11, 2023, retrieved May 1, 2023
  98. ^ "WebGPT: Improving the factual accuracy of language models through web browsing". openai.com. Archived from the original on June 21, 2023. Retrieved July 2, 2023.
  99. ^ Nakano, Reiichiro; Hilton, Jacob; Balaji, Suchir; Wu, Jeff; Ouyang, Long; Kim, Christina; Hesse, Christopher; Jain, Shantanu; Kosaraju, Vineet; Saunders, William; Jiang, Xu; Cobbe, Karl; Eloundou, Tyna; Krueger, Gretchen; Button, Kevin (December 1, 2021). "WebGPT: Browser-assisted question-answering with human feedback". CoRR. arXiv:2112.09332. Archived from the original on July 2, 2023. Retrieved July 2, 2023.
  100. ^ "GPT-4". openai.com. Archived from the original on March 14, 2023. Retrieved May 1, 2023.
  101. ^ OpenAI (March 27, 2023). "GPT-4 Technical Report". arXiv:2303.08774 [cs.CL].
  102. ^ Bubeck, Sébastien; Chandrasekaran, Varun; Eldan, Ronen; Gehrke, Johannes; Horvitz, Eric; Kamar, Ece; Lee, Peter; Lee, Yin Tat; Li, Yuanzhi; Lundberg, Scott; Nori, Harsha; Palangi, Hamid; Ribeiro, Marco Tulio; Zhang, Yi (April 13, 2023). "Sparks of Artificial General Intelligence: Early experiments with GPT-4". arXiv:2303.12712 [cs.CL].
  103. ^ GPT-4 System Card Archived April 7, 2023, at the Wayback Machine, OpenAI, March 23, 2023 (Accessed May 22, 2023).
  104. ^ "Hello GPT-4o". OpenAI. May 13, 2024. Archived from the original on May 14, 2024. Retrieved August 8, 2024.
  105. ^ "Introducing GPT-4.5". OpenAI. February 27, 2025. Archived from the original on March 19, 2025. Retrieved March 18, 2025.
Retrieved from "https://en.wikipedia.org/w/index.php?title=Generative_pre-trained_transformer&oldid=1286395599#Foundational_models"