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Llm як інструмент для емоційної нейтралізації тексту
(ХНУРЕ, 2024) Білоконь, Б. О.
Emotionality in communication can have both positive and negative effects, depending on how it is used and controlled. The aim of this paper is to investigate the effectiveness and accuracy of emotional neutralization of texts using LLM, while preserving semantics and main meaning. The study uses prompt engineering methods to build queries whose answers will have the best results.
Тестування логічних здібностей великих мовних моделей
(ХНУРЕ, 2024) Лавриненко, С. Р.
This work is devoted to investigating the performance of Large Language Models (LLMs) in solving logical problems in Ukrainian, where key words are replaced with nonsensical ones to challenge the models' reliance on prior knowledge. It highlights a study comparing the abilities of four models—ChatGPT 3.5, ChatGPT 4.0, Copilot, and Gemini—across different testing scenarios, including both isolated and contextual problem-solving. The findings reveal that all models significantly outperform random guessing, with ChatGPT 4.0 showing exceptionally high accuracy, suggesting its potential in applications requiring complex logical reasoning.