Машиннное обучение | Наука о данных Библиотека | United States America (US)

Create: 2025-06-10 Update: 2025-06-30 04:38:38

📄 Это исследование оценивает 14 техник prompt-инжиниринга для 10 задач в области Software Engineering, используя 4 разных LLM.

Методология 🔧:

● Эффективность оценивалась с помощью метрик: Accuracy, F1 score, CodeBLEU, BLEU.

● Анализировались лингвистические характеристики prompt'ов: лексическое разнообразие, число токенов и их связь с качеством ответа.

● Применялась контрастивная интерпретация, чтобы выявить ключевые факторы, влияющие на результативность техник.

📌 Выводы помогут понять, какие типы prompt'ов реально работают в инженерных задачах и почему.

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Машиннное обучение | Наука о данных Библиотека

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Машиннное обучение | Наука о данных Библиотека

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📄 Это исследование оценивает 14 техник prompt-инжиниринга для 10 задач в области Software Engineering

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