Everything You Wanted To Know About Chatgpt: Components, Capabilities, Applications, and Opportunities

dc.authorid Heidari, Arash/0000-0003-4279-8551
dc.authorscopusid 57217424609
dc.authorscopusid 59125628000
dc.authorscopusid 7003472739
dc.authorscopusid 55427784900
dc.authorwosid Heidari, Arash/AAK-9761-2021
dc.contributor.author Heidari, Arash
dc.contributor.author Jafari Navimipour, Nima
dc.contributor.author Navimipour, Nima Jafari
dc.contributor.author Zeadally, Sherali
dc.contributor.author Chamola, Vinay
dc.contributor.other Computer Engineering
dc.date.accessioned 2024-06-23T21:37:44Z
dc.date.available 2024-06-23T21:37:44Z
dc.date.issued 2024
dc.department Kadir Has University en_US
dc.department-temp [Heidari, Arash] Halic Univ, Dept Software Engn, Istanbul, Turkiye; [Heidari, Arash] Istanbul Atlas Univ, Fac Engn & Nat Sci, Dept Comp Engn, Istanbul, Turkiye; [Navimipour, Nima Jafari] Kadir Has Univ, Fac Engn & Nat Sci, Dept Comp Engn, TR-34083 Istanbul, Turkiye; [Navimipour, Nima Jafari] Natl Yunlin Univ Sci & Technol, Future Technol Res Ctr, Touliu, Taiwan; [Zeadally, Sherali] Univ Kentucky, Coll Commun & Informat, Lexington, KY USA; [Chamola, Vinay] Birla Inst Technol & Sci BITS, Pilani, India en_US
dc.description Heidari, Arash/0000-0003-4279-8551 en_US
dc.description.abstract Conversational Artificial Intelligence (AI) and Natural Language Processing have advanced significantly with the creation of a Generative Pre-trained Transformer (ChatGPT) by OpenAI. ChatGPT uses deep learning techniques like transformer architecture and self-attention mechanisms to replicate human speech and provide coherent and appropriate replies to the situation. The model mainly depends on the patterns discovered in the training data, which might result in incorrect or illogical conclusions. In the context of open-domain chats, we investigate the components, capabilities constraints, and potential applications of ChatGPT along with future opportunities. We begin by describing the components of ChatGPT followed by a definition of chatbots. We present a new taxonomy to classify them. Our taxonomy includes rule-based chatbots, retrieval-based chatbots, generative chatbots, and hybrid chatbots. Next, we describe the capabilities and constraints of ChatGPT. Finally, we present potential applications of ChatGPT and future research opportunities. The results showed that ChatGPT, a transformer-based chatbot model, utilizes encoders to produce coherent responses. en_US
dc.identifier.citationcount 0
dc.identifier.doi 10.1002/itl2.530
dc.identifier.issn 2476-1508
dc.identifier.scopus 2-s2.0-85194725891
dc.identifier.scopusquality Q3
dc.identifier.uri https://doi.org/10.1002/itl2.530
dc.identifier.uri https://hdl.handle.net/20.500.12469/5740
dc.identifier.wos WOS:001235765500001
dc.language.iso en en_US
dc.publisher John Wiley & Sons Ltd en_US
dc.relation.publicationcategory Diğer en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.scopus.citedbyCount 23
dc.subject ChatGPT en_US
dc.subject conversational artificial intelligence en_US
dc.subject deep learning en_US
dc.subject generative pre-trained transformer en_US
dc.subject large language models en_US
dc.subject natural language processing en_US
dc.subject self-attention mechanisms en_US
dc.title Everything You Wanted To Know About Chatgpt: Components, Capabilities, Applications, and Opportunities en_US
dc.wos.citedbyCount 19
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