AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI

This research paper delves into the operational mechanisms, strengths, and future potential of ChatGPT, an intelligent chatbot developed by OpenAI. ChatGPT's architecture, rooted in the transformer model, enables it to generate contextually relevant text using attention mechanisms and tokenisat...

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Главные авторы:	Lapina, M. A., Лапина, М. А.
Формат:	Статья
Язык:	English
Опубликовано:	Institute of Electrical and Electronics Engineers Inc. 2025
Темы:	AI chatbot Transformative AI Open AI Natural language processing (NLP) ChatGPT Language models
Online-ссылка:	https://dspace.ncfu.ru/handle/123456789/29598
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spelling	ir-123456789-295982025-01-30T14:40:36Z AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI Lapina, M. A. Лапина, М. А. AI chatbot Transformative AI Open AI Natural language processing (NLP) ChatGPT Language models This research paper delves into the operational mechanisms, strengths, and future potential of ChatGPT, an intelligent chatbot developed by OpenAI. ChatGPT's architecture, rooted in the transformer model, enables it to generate contextually relevant text using attention mechanisms and tokenisation. While excelling in generative capability and versatility, the paper highlights challenges, including context understanding and sensitivity to phrasing, sparking discussions on refinement strategies. The study explores opportunities for efficient, prompt engineering, emphasising tokenization strategies to optimise interactions within ChatGPT's token limits. Future advancements envision enhanced context understanding, reduced sensitivity to phrasing, and ethical considerations, addressing verbosity and response diversity concerns. A comparative analysis with competing models like Google's Bard and Meta's LLaMA provides insights into their architectures, parameters, strengths, weaknesses, and target use cases. The paper concludes by emphasising ChatGPT's transformative impact on AI, shaping a future marked by interdisciplinary applications, ethical considerations, and user-centric design. 2025-01-30T14:39:45Z 2025-01-30T14:39:45Z 2024 Статья Kumar, H., Damle, M., Natraj, N.A., Afzal, A.A., Lapina, M. AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI // 2024 6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024. - 2024. - DOI: 10.1109/ISAECT64333.2024.10799591 https://dspace.ncfu.ru/handle/123456789/29598 en 2024 6th International Symposium on Advanced Electrical and Communication Technologies, ISAECT 2024 application/pdf Institute of Electrical and Electronics Engineers Inc.
institution	СКФУ
collection	Репозиторий
language	English
topic	AI chatbot Transformative AI Open AI Natural language processing (NLP) ChatGPT Language models
spellingShingle	AI chatbot Transformative AI Open AI Natural language processing (NLP) ChatGPT Language models Lapina, M. A. Лапина, М. А. AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
description	This research paper delves into the operational mechanisms, strengths, and future potential of ChatGPT, an intelligent chatbot developed by OpenAI. ChatGPT's architecture, rooted in the transformer model, enables it to generate contextually relevant text using attention mechanisms and tokenisation. While excelling in generative capability and versatility, the paper highlights challenges, including context understanding and sensitivity to phrasing, sparking discussions on refinement strategies. The study explores opportunities for efficient, prompt engineering, emphasising tokenization strategies to optimise interactions within ChatGPT's token limits. Future advancements envision enhanced context understanding, reduced sensitivity to phrasing, and ethical considerations, addressing verbosity and response diversity concerns. A comparative analysis with competing models like Google's Bard and Meta's LLaMA provides insights into their architectures, parameters, strengths, weaknesses, and target use cases. The paper concludes by emphasising ChatGPT's transformative impact on AI, shaping a future marked by interdisciplinary applications, ethical considerations, and user-centric design.
format	Статья
author	Lapina, M. A. Лапина, М. А.
author_facet	Lapina, M. A. Лапина, М. А.
author_sort	Lapina, M. A.
title	AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
title_short	AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
title_full	AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
title_fullStr	AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
title_full_unstemmed	AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI
title_sort	ai-driven natural language processing: chatgpt's potential and future advancements in generative ai
publisher	Institute of Electrical and Electronics Engineers Inc.
publishDate	2025
url	https://dspace.ncfu.ru/handle/123456789/29598
work_keys_str_mv	AT lapinama aidrivennaturallanguageprocessingchatgptspotentialandfutureadvancementsingenerativeai AT lapinama aidrivennaturallanguageprocessingchatgptspotentialandfutureadvancementsingenerativeai
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AI-driven Natural Language Processing: ChatGPT's Potential and Future Advancements in Generative AI

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