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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="en"><front><journal-meta><journal-id journal-id-type="publisher-id">scieditor</journal-id><journal-title-group><journal-title xml:lang="en">Science Editor and Publisher</journal-title><trans-title-group xml:lang="ru"><trans-title>Научный редактор и издатель</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">2542-0267</issn><issn pub-type="epub">2541-8122</issn><publisher><publisher-name>АНРИ</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.24069/SEP-24-11</article-id><article-id custom-type="elpub" pub-id-type="custom">scieditor-413</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SCIENTIFIC COMMUNICATIONS AND SCIENTIFIC JOURNALS</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>НАУЧНЫЕ КОММУНИКАЦИИ И НАУЧНЫЕ ЖУРНАЛЫ</subject></subj-group></article-categories><title-group><article-title>Scientific publications and large language models: will neural network understand Russian science?</article-title><trans-title-group xml:lang="ru"><trans-title>Научные публикации и большие языковые модели: поймет ли нейросеть российскую науку?</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0000-0002-8217-2302</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Зашихина</surname><given-names>И. М.</given-names></name><name name-style="western" xml:lang="en"><surname>Zashikhina</surname><given-names>I. M.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Инга Михайловна Зашихина, кандидат философских наук, доцент</p><p>Архангельск</p></bio><bio xml:lang="en"><p>Inga M. Zashikhina, Cand. Sci. (Philos.), Associate Professor</p><p>Arkhangelsk</p></bio><email xlink:type="simple">zashikhinaim@mail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Северный (Арктический) федеральный университет имени М. В. Ломоносова<country>Россия</country></aff><aff xml:lang="en">Northern (Arctic) Federal University named&#13;
after M.V. Lomonosov<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2024</year></pub-date><pub-date pub-type="epub"><day>29</day><month>10</month><year>2024</year></pub-date><volume>9</volume><issue>1</issue><issue-title>Supplement 2</issue-title><fpage>231</fpage><lpage>246</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Zashikhina I.M., 2024</copyright-statement><copyright-year>2024</copyright-year><copyright-holder xml:lang="ru">Зашихина И.М.</copyright-holder><copyright-holder xml:lang="en">Zashikhina I.M.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://www.scieditor.ru/jour/article/view/413">https://www.scieditor.ru/jour/article/view/413</self-uri><abstract><p>The impact of artificial intelligence technologies, neural networks, and chatbots on science and education has induced widespread discussion in the academic community. It is no longer possible to contain the use of neural networks such as ChatGPT for writing texts, including scientific texts. The present study is done in a qualitative approach. The aim of the study is to analyze the application of large language models on the example of ChatGPT in the scientific publishing activities of Russian scientists. The practice of using chatbots does not always satisfy the user and the scientific community as a whole. On the one hand, the user is confronted with the lack of the requested information. On the other hand, the scientific community, and especially editors and readers of scientific journals, question the feasibility of neural networks due to the shortcomings of large language models that have been widely disputed in scientific publications. This study shows that there is another reason to distrust neural networks. Incompleteness and opacity of the information produced by artificial intelligence is related to the texts on which neural networks are trained. For Russian science, this problem poses a serious threat, since popular artificial intelligence companies use mostly English-language texts to teach their neural nets. The author puts forward the opinion that the social and humanitarian knowledge produced in modern Russia remains outside the scope of texts used for training neural networks. This point of view is supported by research by Russian scientists on Arctic governance. The data is absent in the English-language texts of ChatGPT but reflected in Russian-language publications. </p></abstract><trans-abstract xml:lang="ru"><p>Влияние технологий искусственного интеллекта, нейросетей, чат-ботов на науку и образование вызвало широкую дискуссию в академическом сообществе. Уже не представляется возможным сдержать использование нейросетей, таких как ChatGPT, для написания текстов, в том числе научных. Настоящее исследование выполнено в рамках качественного подхода. Целью исследования является анализ применения больших языковых моделей на примере ChatGPT в научно-публикационной деятельности российских ученых. Практика применения чат-ботов далеко не всегда приносит удовлетворительный результат как для пользователя, так и для научного сообщества в целом. Пользователь зачастую сталкивается с отсутствием запрашиваемой информации в ответе нейросети, которое является результатом ограничения выборки текстов для ее обучения. Научное сообщество, и особенно редакторы и читатели научных журналов, ставит под сомнение возможность применения нейросетей в связи с недостатками больших языковых моделей, получившими широкое освещение в научных публикациях. Данное исследование показывает, что есть еще одна причина не доверять нейросетям. Неполнота и непрозрачность производимой искусственным интеллектом информации связаны с текстами, на которых обучаются нейросети. Для российской науки данная проблема несет серьезную угрозу, поскольку популярные компании – производители искусственного интеллекта для этой цели используют преимущественно англоязычные тексты. Автор выдвигает мнение о том, что социально-гуманитарное знание, производимое в современной России, остается за пределами текстов, применяемых для обучения нейросетей. Такая точка зрения подтверждается данными исследования, посвященного управлению Российской Арктикой. Информация по данной тематике имеет отражение в русскоязычных публикациях, но отсутствует в ответах англоязычных ChatGPT. </p></trans-abstract><kwd-group xml:lang="ru"><kwd>искусственный интеллект</kwd><kwd>нейросети</kwd><kwd>большие языковые модели</kwd><kwd>обучение больших языковых моделей</kwd><kwd>научные публикации</kwd><kwd>текст научной статьи</kwd><kwd>русскоязычные тексты</kwd></kwd-group><kwd-group xml:lang="en"><kwd>artificial intelligence</kwd><kwd>neural networks</kwd><kwd>large language models</kwd><kwd>large language models learning</kwd><kwd>scientific publications</kwd><kwd>scientific article text</kwd><kwd>Russian-language texts</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Ивахненко Е.Н., Никольский В.С. ChatGPT в высшем образовании и науке: угроза или ценный ресурс? 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