Исследование эффективности промпт-инжиниринга и квантованных LLM в создании структуры академических курсов
DOI:
https://doi.org/10.32603/2071-2340-2024-1-32-44Ключевые слова:
большие языковые модели, промпт-инжиниринг, квантованные модели, few-shot, zero-shot, chain-of-thoughtАннотация
В данной статье представлены итоги эксперимента по применению больших языковых моделей (LLM) для создания структуры университетских курсов. Для формирования запросов к LLM использовались такие методы промпт-инжиниринга, как zero-shot, few-shot, chain-of-thought и tree-of-thought. Для эксперимента преимущественно использовались квантованные модели, такие как mistral-7b-instruct, mixtral-8x7b-instruct, openchat_3.5, saiga2_13b, starling-lm-7b-alpha, tinyllama и другие. Сгенерированные ими структуры курсов сравнивались с данными, полученными с помощью ChatGPT-4. Модели openchat_3.5.q5_k_m и starling-lm-7b-alpha.q5_k_m показали сопоставимое с ChatGPT-4 качество генерации рабочих программ дисциплин. Эксперимент подчеркивает возможности применения LLM в сфере образования и указывает на перспективные направления для дальнейших исследований.
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