Исследование эффективности промпт-инжиниринга и квантованных LLM в создании структуры академических курсов
Exploring the Effectiveness of Prompt Engineering and Quantized Large Language Models in the Development of Academic Courses
Exploring the Effectiveness of Prompt Engineering and Quantized Large Language Models in the Development of Academic Courses
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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. С. 32-44.
This article presents the outcomes of an experiment employing large language models (LLMs) in the development of university course structures. Various prompt engineering methods, including zero-shot, few-shot, chain-of-thought, and tree-of-thought, were employed to formulate queries to LLMs. Primarily, quantized models such as mistral-7b-instruct, mixtral-8x7b-instruct, openchat_3.5, saiga2_13b, starling-lm-7b-alpha, tinyllama, among others, were utilized for the experiment. The generated course structures were compared with data obtained from ChatGPT-4. Models openchat_3.5.q5_k_m and starling-lm-7b-alpha.q5_k_m demonstrated comparable quality in generating educational program structures to ChatGPT-4. The experiment underscores the potential applications of LLMs in the field of education and highlights promising directions for further research.
This article presents the outcomes of an experiment employing large language models (LLMs) in the development of university course structures. Various prompt engineering methods, including zero-shot, few-shot, chain-of-thought, and tree-of-thought, were employed to formulate queries to LLMs. Primarily, quantized models such as mistral-7b-instruct, mixtral-8x7b-instruct, openchat_3.5, saiga2_13b, starling-lm-7b-alpha, tinyllama, among others, were utilized for the experiment. The generated course structures were compared with data obtained from ChatGPT-4. Models openchat_3.5.q5_k_m and starling-lm-7b-alpha.q5_k_m demonstrated comparable quality in generating educational program structures to ChatGPT-4. The experiment underscores the potential applications of LLMs in the field of education and highlights promising directions for further research.
Ключевые слова: большие языковые модели, промпт-инжиниринг, квантованные модели, few-shot, zero-shot, chain-of-thought.
Keywords: Large Language Models, Prompt Engineering, Quantized Models, few-shot, zero-shot, chain-of-thought.
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