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Риски использования синтетических данных для обучения больших языковых моделей по данным Meta AI

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Познакомьтесь с TurtleBench: Уникальная система оценки ИИ для анализа языковых моделей с помощью реальных задач “Да/Нет”.

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Дифференцируемое адаптивное объединение: новый подход ИИ к интеграции моделей

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Исследователи Google AI предложили новые методы улучшения работы LLM с длинным контекстом в генерации с использованием данных из поиска.

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Mistral AI представляет Les Ministraux: Ministral 3B и Ministral 8B – новое поколение AI для устройств.

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АвтоDAN-Turbo: Метод взлома LLM с постоянным агентом

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IGNN-Solver: Новый графовый нейронный решатель для неявных графовых нейронных сетей

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DeepMind представила новый набор данных для оценки устойчивости агентов обучения с подкреплением к визуальным помехам

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Исследование Google AI: случайная выборка цепей для оценки производительности квантовых компьютеров в условиях шума

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Оптимизация предпочтений мышления: как это улучшает языковые модели для логики, маркетинга и творчества

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Орфрус: Модель RNA на основе Мамбы для предсказания свойств РНК

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Интерфейс воплощенного агента: AI-рамка для оценки больших языковых моделей в принятии решений.

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SeedLM: Метод сжатия после обучения, использующий псевдослучайные генераторы для эффективного кодирования и сжатия весов LLM

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Google AI представил Gemma-APS: набор моделей Gemma для сегментации текста на предложения.

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Новое исследование OpenAI показывает, как имена пользователей влияют на ответы ChatGPT.

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Neural Magic представила Machete: новый ядро GEMM для графических процессоров NVIDIA Hopper с смешанным вводом.

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Google AI представила новые инструменты для улучшения логики в языковых моделях.

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Новая технология для развертывания небольших языковых моделей от Predibase

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AFlow: Новый искусственный интеллект для автоматизации рабочих процессов

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MentalArena: ИИ для самостановки, обучающий языковые модели для диагностики и лечения психических расстройств.

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