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Исследователи из KAIST и Google AI представляют метод BCD для повышения эффективности и связности языковых моделей.

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Искусственный интеллект для улучшения моделирования распространения видов с помощью геопространственных данных и глубоких нейронных сетей.

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CoAgents CopilotKit: Простое подключение агентов LangGraph к людям.

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Новый метод тестирования биомедицинских моделей видения: RadEdit.

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Метод обучения искусственного интеллекта для эффективного обучения LLM на больших наборах данных.

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Исследование усвоения и сохранения фактических знаний в больших языковых моделях.

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Python-фреймворк для быстрой и масштабируемой обработки данных LLM.

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Улучшение логического мышления в больших языковых моделях через добавление предикативной логики.

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Метод улучшает внимание нейросети без изменения параметров

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Обучение моделей на изображениях для улучшения понимания текста

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Google выпустил набор данных FRAMES для тестирования приложений Retrieval-Augmented Generation (RAG)

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Искусственный интеллект, использующий небольшое количество примеров для создания качественных текстовых вложений

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NotebookLM добавляет интеграцию с аудио и YouTube для улучшения обмена аудио-презентациями.

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10 способов снижения затрат на вывод больших языковых моделей

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Простой и мощный подход к непрерывному обучению без образцов.

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Новая технология для прогнозирования больших объемов данных.

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Фреймворк BioMed-VITAL для настройки биомедицинского визуального обучения.

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Новый подход к улучшению эффективности LLM в многоязычных средах.

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Проблема моделирования данных: аналитические выводы и стратегии устранения.

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Методы разделения текста для улучшения генерации (RAG): Полное руководство по оптимизации сегментации текста.

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