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dc.contributor.authorZhang Xueqing-
dc.contributor.authorLiang Hong-
dc.date.accessioned2024-06-17T09:16:02Z-
dc.date.available2024-06-17T09:16:02Z-
dc.date.issued2024-
dc.identifier.citationZhang Xueqing. Cross-Model Comparison: The Effectiveness of Large Language Models in Translating Political Texts / Zhang Xueqing, Liang Hong // Актуальні проблеми філології і професійної підготовки фахівців у полікультурному просторі: Міжнародний журнал. – Випуск 7. – Харбін : Харбінський інженерний університет, 2024 ‒ С.42-44uk
dc.identifier.uridspace.pdpu.edu.ua/jspui/handle/123456789/19361-
dc.description.abstractThe swift evolution of Large Language Model (LLM) technologies has underscored their expansive applicability across a broad spectrum of disciplines, notably within the realms of natural language processing and machine translation. Machine translation services powered by large language models surpass ordinary machine translation, making their performance in terms of translation quality, proofreading ability, and sentence optimization a new focal point in the field of machine translation and translation studies.uk
dc.language.isootheruk
dc.publisherДержавний заклад «Південноукраїнський національний педагогічний університет імені К. Д. Ушинського»uk
dc.subjectLarge Language Modelsuk
dc.subjectAutomatic Evaluation Metricsuk
dc.subjectPolitical Texts Translationuk
dc.titleCross-Model Comparison: The Effectiveness of Large Language Models in Translating Political Textsuk
dc.typeArticleuk
Appears in Collections:2024 Вип. 7

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