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V. Gadiraju, S. Kane, S. Dev, A. Taylor, D. Wang, E. Denton, and R. Brewer, ““I wouldn’t say offensive but...”: Disability-Centered Perspectives on Large Language Models,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, ser. FAccT ‘23. New York, NY, USA: Association for Computing Machinery, 2023, p. 205–216. doi: https://doi.org/10.1145/3593013.3593989.

N. S. Patil, R. S. Huang, C. B. van der Pol, and N. Larocque, “Comparative Performance of ChatGPT and Bard in a Text-Based Radiology Knowledge Assessment,” Canadian Association of Radiologists Journal, 2023. doi: https://doi.org/10.1177/08465371231193716.

B. Kim, J. Seo, and M.-W. Koo, “Randomly Wired Network Based on RoBERTa and Dialog History Attention for Response Selection,” IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 29, pp. 2437–2442, 2021. doi: https://doi.org/10.1109/TASLP.2021.3077119.

J. Zhang, J. Zhang, S. Ma, J. Yang, and G. Gui, “Chatbot design method using hybrid word vector expression model based on real telemarketing data,” KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1400–1418, 2020. doi: https://doi.org/10.3837/TIIS.2020.04.001.

S. S. Abdullahi, S. Yiming, A. Abdullahi, and U. Aliyu, “Open Domain Chatbot Based on Attentive End-to-End Seq2Seq Mechanism,” in Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ser. ACAI ‘19. New York, NY, USA: Association for Computing Machinery, 2020, p. 339–344. doi: https://doi.org/10.1145/3377713.3377773.

A. G. Usigan, M. I. Salomeo, G. J. L. J. Zafe, C. J. Centeno, A. A. R. C. Sison, and A. G. Bitancor, “Implementation of an Undergraduate Admission Chatbot Using Microsoft Azure’s Question Answering and Bot Framework,” in Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference, ser. AICCC ‘22. New York, NY, USA: Association for Computing Machinery, 2023, p. 240–245. doi: https://doi.org/10.1145/3582099.3582135.

E. Ruane, R. Young, and A. Ventresque, “Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy,” in Companion Proceedings of the 25th International Conference on Intelligent User Interfaces, ser. IUI ‘20 Companion. New York, NY, USA: Association for Computing Machinery, 2020, p. 63–64. doi: https://doi.org/10.1145/3379336.3381494.

N. Bhartiya, N. Jangid, S. Jannu, P. Shukla, and R. Chapaneri, “Artificial Neural Network Based University Chatbot System,” in 2019 IEEE Bombay Section Signature Conference, IBSSC 2019, vol. 2019January. Institute of Electrical and Electronics Engineers Inc., 2019, Conference paper. doi: https://doi.org/10.1109/IBSSC47189.2019.8973095.

B. Hancock, A. Bordes, P.-E. Mazaré, and J. Weston, “Learning from dialogue after deployment: Feed yourself, Chatbot!” in ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020, Conference paper, pp. 3667–3684.

M. N. Sreedhar, K. Ni, and S. Reddy, “Learning improvised chatbots from adversarial modifications of natural language feedback,” in Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, 2020, pp. 2445–2453.

S.-W. Lee and W.-J. Choi, “Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive review of opportunities and limitations,” Anesthesia and Pain Medicine, vol. 18, no. 3, pp. 244–251, 2023. doi: https://doi.org/10.17085/apm.23056.

C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,” Education Sciences, vol. 13, no. 4, p. 410, 2023. [Online]. Available: https://doi.org/10.3390/educsci13040410

A. Pack and J. Maloney, “Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI’s ChatGPT,” TESOL Quarterly, vol. 57, no. 4, pp. 1571–1582, 2023. doi: https://doi.org/10.1002/tesq.3253.

J. Chervenak, H. Lieman, M. Blanco-Breindel, and S. Jindal, “The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations,” Fertility and Sterility, vol. 120, no. 3, pp. 575–583, 2023. doi: https://doi.org/10.1016/j.fertnstert.2023.05.151.

A. V. Riabko and T. A. Vakaliuk, “Physics on autopilot: exploring the use of an AI assistant for independent problem-solving practice,” Educational Technology Quarterly, vol. 2024, no. 1, p. 56–75, Mar. 2024. doi: https://doi.org/10.55056/etq.671.

OpenAI, “Introducing ChatGPT,” Nov. 2022. [Online]. Available: https://openai.com/blog/chatgpt

DeepLearning.AI, “Search | The Batch | AI News & Insights,” Dec. 2023. [Online]. Available: https://www.deeplearning.ai/search/

“Big Bot Makes Small Talk: A research summary of Facebook’s Generative BST chatbot,” May 2020. [Online]. Available: https://www.deeplearning.ai/the-batch/big-bot-makes-small-talk/

“Bot Comic: How Google’s Meena chatbot developed a sense of humor,” Feb. 2020. [Online]. Available: https://www.deeplearning.ai/the-batch/bot-comic/

“Chatbots for Productivity,” Sep. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/microsoft-extends-copilot-365-windows/

“China Chases Chatbots,” Mar. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/chinese-tech-companies-race-to-cash-in-on-chatgpt-fever/

“Search War!” Feb. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/google-and-microsoft-both-announce-ai-powered-search/

“Chatbots Disagree on Covid-19: Medical chatbots oﬀered conﬂicting Covid advice,” Apr. 2020. [Online]. Available: https://www.deeplearning.ai/the-batch/chatbots-disagree-on-covid-19/

“Language Models, Extended: Large language models grew more reliable and less biased in 2022,” Dec. 2022. [Online]. Available: https://www.deeplearning.ai/the-batch/language-models-grew-more-reliable-and-less-biased-in-2022/

“Cost Containment for Generative AI: Microsoft’s quest to reduce the size and cost of language models,” Oct. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/microsofts-quest-to-reduce-the-size-and-cost-of-language-models/

“What We Know — and Don’t Know — About Foundation Models: A new Stanford index to assess the transparency of leading AI models,” Nov. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/a-new-stanford-index-to-assess-the-transparency-of-leading-ai-models/

Elsevier B.V., “Scopus - Document search | Signed in,” 2023. [Online]. Available: https://www.scopus.com/search/form.uri?display=basic#basic

N. J. Van Eck and L. Waltman, VOSviewer Manual. Universiteit Leiden, 2023. [Online]. Available: https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.20.pdf

Centre for Science and Technology Studies, Leiden University, The Netherlands, “VOSviewer - Visualizing scientific landscapes,” 2023. [Online]. Available: https://www.vosviewer.com/" xml:lang="uk" /> <meta name="DCTERMS.abstract" content="This bibliometric analysis aims to identify current research directions and priorities in the field of chatbot training – software agents capable of natural language dialogue. The study is based on the analysis of 549 scientific sources from the Scopus database on this topic. The analysis revealed a steady increase in relevant publications starting from 2018, indicating a growing relevance of this subject area in recent years. Based on a cluster analysis of keywords, four main research areas were identified: natural language processing, application of relevant technologies in various spheres of society, use of machine learning methods for natural language processing, and application of chatbots in education and services. In the field of natural language processing, the focus of current research is on computational linguistics, language modeling and machine comprehension, particularly speech recognition tasks. Leading research on artificial intelligence applications in this area concerns the responsible and ethical use of modern large language models and conversational agents, such as ChatGPT, in education and healthcare. Machine learning methods are actively being developed for creating virtual intelligent assistants, natural language user interfaces, and other natural language processing systems, including for diagnostic tasks in medicine. Key applications of chatbots are identified in adaptive learning systems, knowledge management, and customer service. Based on the analysis, the most significant concepts in each of the studied areas are defined to outline priorities for further research in the field of chatbot training. Future work involves conducting a systematic literature review with the automation of certain stages using large language models. In particular, these models will be employed to automatically classify study abstracts according to inclusion/exclusion criteria during the screening phase. Automating systematic review stages using artificial intelligence opens up significant prospects for accelerating scientific research, particularly in the education field based on an evidence-based approach." xml:lang="uk" /> <meta name="DC.language" content="uk" xml:lang="uk" scheme="DCTERMS.RFC1766" /> <meta name="DC.publisher" content="Institute for Digitalisation of Education of NAES of Ukraine" xml:lang="uk" /> <meta name="DC.subject" content="chatbot training" xml:lang="uk" /> <meta name="DC.subject" content="natural language processing" xml:lang="uk" /> <meta name="DC.subject" content="machine learning" xml:lang="uk" /> <meta name="DC.subject" content="bibliometric analysis" xml:lang="uk" /> <meta name="DC.subject" content="systematic literature review" xml:lang="uk" /> <meta name="DC.subject" content="large language models" xml:lang="uk" /> <meta name="DC.title" content="Bibliometric analysis of chatbot training research: key concepts and trends" xml:lang="uk" /> <meta name="DCTERMS.alternative" content="Бібліометричний аналіз досліджень з навчання чат-ботів: ключові поняття та тенденції" xml:lang="uk" /> <meta name="DC.type" content="Article" xml:lang="uk" /> <meta content="chatbot training; natural language processing; machine learning; bibliometric analysis; systematic literature review; large language models" name="citation_keywords" /> <meta content="2076-8184" name="citation_issn" /> </head>    <body> <div id="ds-main"> <div id="ds-header-wrapper"> <div class="clearfix" id="ds-header"> <a id="ds-header-logo-link" href="/xmlui/"> <span id="ds-header-logo"> </span> <span id="ds-header-logo-text">DSpace Repository</span> </a> <h1 xmlns:i18n="http://apache.org/cocoon/i18n/2.1" class="pagetitle visuallyhidden">Bibliometric analysis of chatbot training research: key concepts and trends</h1> <div id="ds-user-box"> <p> <a href="/xmlui/login">Login</a> </p> </div> </div> </div> <div xmlns:i18n="http://apache.org/cocoon/i18n/2.1" id="ds-trail-wrapper"> <ul id="ds-trail"> <li class="ds-trail-link first-link "> <a href="/xmlui/">DSpace Home</a> </li> <li xmlns:i18n="http://apache.org/cocoon/i18n/2.1" xmlns="http://di.tamu.edu/DRI/1.0/" class="ds-trail-arrow">→</li> <li class="ds-trail-link "> <a href="/xmlui/handle/0564/46">Електронні публікації</a> </li> <li class="ds-trail-arrow">→</li> <li class="ds-trail-link "> <a href="/xmlui/handle/0564/47">Фізико-математичний факультет</a> </li> <li class="ds-trail-arrow">→</li> <li class="ds-trail-link "> <a href="/xmlui/handle/0564/56">Кафедра інформатики та прикладної математики</a> </li> <li class="ds-trail-arrow">→</li> <li class="ds-trail-link last-link">View Item</li> </ul> </div> <div xmlns:i18n="http://apache.org/cocoon/i18n/2.1" xmlns="http://di.tamu.edu/DRI/1.0/" class="hidden" id="no-js-warning-wrapper"> <div id="no-js-warning"> <div class="notice failure">JavaScript is disabled for your browser. Some features of this site may not work without it.</div> </div> </div> <div id="ds-content-wrapper"> <div class="clearfix" id="ds-content"> <div id="ds-body"> <div id="aspect_artifactbrowser_ItemViewer_div_item-view" class="ds-static-div primary">  <div xmlns:oreatom="http://www.openarchives.org/ore/atom/" xmlns:ore="http://www.openarchives.org/ore/terms/" xmlns:atom="http://www.w3.org/2005/Atom" class="item-summary-view-metadata"> <h1>Bibliometric analysis of chatbot training research: key concepts and trends</h1> <div class="simple-item-view-authors"> <span>Liashenko, Roman</span>; <span>Семеріков, Сергій Олексійович</span> </div> <div class="simple-item-view-other"> <span class="bold">URI:</span> <span xmlns:i18n="http://apache.org/cocoon/i18n/2.1"> <a href="https://journal.iitta.gov.ua/index.php/itlt/article/view/5622">https://journal.iitta.gov.ua/index.php/itlt/article/view/5622</a> <br /> <a href="https://doi.org/10.33407/itlt.v101i3.5622">https://doi.org/10.33407/itlt.v101i3.5622</a> <br /> <a href="https://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10380">http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/10380</a> </span> </div> <div class="simple-item-view-other"> <span class="bold">Date:</span> <span xmlns:i18n="http://apache.org/cocoon/i18n/2.1">2024-06-28</span> </div> <div class="simple-item-view-description"> <h3>Abstract:</h3> <div xmlns:i18n="http://apache.org/cocoon/i18n/2.1">This bibliometric analysis aims to identify current research directions and priorities in the field of chatbot training – software agents capable of natural language dialogue. The study is based on the analysis of 549 scientific sources from the Scopus database on this topic. The analysis revealed a steady increase in relevant publications starting from 2018, indicating a growing relevance of this subject area in recent years. Based on a cluster analysis of keywords, four main research areas were identified: natural language processing, application of relevant technologies in various spheres of society, use of machine learning methods for natural language processing, and application of chatbots in education and services. In the field of natural language processing, the focus of current research is on computational linguistics, language modeling and machine comprehension, particularly speech recognition tasks. Leading research on artificial intelligence applications in this area concerns the responsible and ethical use of modern large language models and conversational agents, such as ChatGPT, in education and healthcare. Machine learning methods are actively being developed for creating virtual intelligent assistants, natural language user interfaces, and other natural language processing systems, including for diagnostic tasks in medicine. Key applications of chatbots are identified in adaptive learning systems, knowledge management, and customer service. Based on the analysis, the most significant concepts in each of the studied areas are defined to outline priorities for further research in the field of chatbot training. Future work involves conducting a systematic literature review with the automation of certain stages using large language models. In particular, these models will be employed to automatically classify study abstracts according to inclusion/exclusion criteria during the screening phase. Automating systematic review stages using artificial intelligence opens up significant prospects for accelerating scientific research, particularly in the education field based on an evidence-based approach.</div> </div> <div class="simple-item-view-description"> <h3 class="bold">Description:</h3> <div xmlns:i18n="http://apache.org/cocoon/i18n/2.1">K. Peyton and S. Unnikrishnan, “A comparison of chatbot platforms with the state-of-the-art sentence BERT for answering online student FAQs,” Results in Engineering, vol. 17, 2023. doi: https://doi.org/10.1016/j.rineng.2022.100856.  V. Gadiraju, S. Kane, S. Dev, A. Taylor, D. Wang, E. Denton, and R. Brewer, ““I wouldn’t say offensive but...”: Disability-Centered Perspectives on Large Language Models,” in Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency, ser. FAccT ‘23. New York, NY, USA: Association for Computing Machinery, 2023, p. 205–216. doi: https://doi.org/10.1145/3593013.3593989.  N. S. Patil, R. S. Huang, C. B. van der Pol, and N. Larocque, “Comparative Performance of ChatGPT and Bard in a Text-Based Radiology Knowledge Assessment,” Canadian Association of Radiologists Journal, 2023. doi: https://doi.org/10.1177/08465371231193716.  B. Kim, J. Seo, and M.-W. Koo, “Randomly Wired Network Based on RoBERTa and Dialog History Attention for Response Selection,” IEEE/ACM Transactions on Audio Speech and Language Processing, vol. 29, pp. 2437–2442, 2021. doi: https://doi.org/10.1109/TASLP.2021.3077119.  J. Zhang, J. Zhang, S. Ma, J. Yang, and G. Gui, “Chatbot design method using hybrid word vector expression model based on real telemarketing data,” KSII Transactions on Internet and Information Systems, vol. 14, no. 4, pp. 1400–1418, 2020. doi: https://doi.org/10.3837/TIIS.2020.04.001.  S. S. Abdullahi, S. Yiming, A. Abdullahi, and U. Aliyu, “Open Domain Chatbot Based on Attentive End-to-End Seq2Seq Mechanism,” in Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ser. ACAI ‘19. New York, NY, USA: Association for Computing Machinery, 2020, p. 339–344. doi: https://doi.org/10.1145/3377713.3377773.  A. G. Usigan, M. I. Salomeo, G. J. L. J. Zafe, C. J. Centeno, A. A. R. C. Sison, and A. G. Bitancor, “Implementation of an Undergraduate Admission Chatbot Using Microsoft Azure’s Question Answering and Bot Framework,” in Proceedings of the 2022 5th Artificial Intelligence and Cloud Computing Conference, ser. AICCC ‘22. New York, NY, USA: Association for Computing Machinery, 2023, p. 240–245. doi: https://doi.org/10.1145/3582099.3582135.  E. Ruane, R. Young, and A. Ventresque, “Training a Chatbot with Microsoft LUIS: Effect of Intent Imbalance on Prediction Accuracy,” in Companion Proceedings of the 25th International Conference on Intelligent User Interfaces, ser. IUI ‘20 Companion. New York, NY, USA: Association for Computing Machinery, 2020, p. 63–64. doi: https://doi.org/10.1145/3379336.3381494.  N. Bhartiya, N. Jangid, S. Jannu, P. Shukla, and R. Chapaneri, “Artificial Neural Network Based University Chatbot System,” in 2019 IEEE Bombay Section Signature Conference, IBSSC 2019, vol. 2019January. Institute of Electrical and Electronics Engineers Inc., 2019, Conference paper. doi: https://doi.org/10.1109/IBSSC47189.2019.8973095.  B. Hancock, A. Bordes, P.-E. Mazaré, and J. Weston, “Learning from dialogue after deployment: Feed yourself, Chatbot!” in ACL 2019 - 57th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2020, Conference paper, pp. 3667–3684.  M. N. Sreedhar, K. Ni, and S. Reddy, “Learning improvised chatbots from adversarial modifications of natural language feedback,” in Findings of the Association for Computational Linguistics Findings of ACL: EMNLP 2020, 2020, pp. 2445–2453.  S.-W. Lee and W.-J. Choi, “Utilizing ChatGPT in clinical research related to anesthesiology: a comprehensive review of opportunities and limitations,” Anesthesia and Pain Medicine, vol. 18, no. 3, pp. 244–251, 2023. doi: https://doi.org/10.17085/apm.23056.  C. K. Lo, “What Is the Impact of ChatGPT on Education? A Rapid Review of the Literature,” Education Sciences, vol. 13, no. 4, p. 410, 2023. [Online]. Available: https://doi.org/10.3390/educsci13040410  A. Pack and J. Maloney, “Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI’s ChatGPT,” TESOL Quarterly, vol. 57, no. 4, pp. 1571–1582, 2023. doi: https://doi.org/10.1002/tesq.3253.  J. Chervenak, H. Lieman, M. Blanco-Breindel, and S. Jindal, “The promise and peril of using a large language model to obtain clinical information: ChatGPT performs strongly as a fertility counseling tool with limitations,” Fertility and Sterility, vol. 120, no. 3, pp. 575–583, 2023. doi: https://doi.org/10.1016/j.fertnstert.2023.05.151.  A. V. Riabko and T. A. Vakaliuk, “Physics on autopilot: exploring the use of an AI assistant for independent problem-solving practice,” Educational Technology Quarterly, vol. 2024, no. 1, p. 56–75, Mar. 2024. doi: https://doi.org/10.55056/etq.671.  OpenAI, “Introducing ChatGPT,” Nov. 2022. [Online]. Available: https://openai.com/blog/chatgpt  DeepLearning.AI, “Search | The Batch | AI News & Insights,” Dec. 2023. [Online]. Available: https://www.deeplearning.ai/search/  “Big Bot Makes Small Talk: A research summary of Facebook’s Generative BST chatbot,” May 2020. [Online]. Available: https://www.deeplearning.ai/the-batch/big-bot-makes-small-talk/  “Bot Comic: How Google’s Meena chatbot developed a sense of humor,” Feb. 2020. [Online]. Available: https://www.deeplearning.ai/the-batch/bot-comic/  “Chatbots for Productivity,” Sep. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/microsoft-extends-copilot-365-windows/  “China Chases Chatbots,” Mar. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/chinese-tech-companies-race-to-cash-in-on-chatgpt-fever/  “Search War!” Feb. 2023. [Online]. Available: https://www.deeplearning.ai/the-batch/google-and-microsoft-both-announce-ai-powered-search/  “Chatbots Disagree on Covid-19: Medical chatbots oﬀered conﬂicting Covid advice,” Apr. 2020. [Online]. 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