The Power of Personalized Datasets: Advancing Chinese Composition Writing for Elementary School through Targeted Model Fine-Tuning
Wenyi Xie, Jiachen Li, Yi Mu, Hanyu Zhang, Shiwen Zhao, Xinran Zheng · 2024
Today the Large Language Model profoundly affects the way we work in all walks of life, as well as the way we teach in the field of education. In this paper, we focus on the Large Language Model we designed for composition education in elementary school language. We focus on the accurate understanding of Chinese vocabulary and the adaptation of language vocabulary and language structures for the domain of elementary school students, which is currently missing in mainstream LLMs. At the same time, we also pay attention to the current educational concerns about the misuse of the LLM, and target the sensitive questioning designed about the direct generation of composition answers. In the process, we collected datasets related to composition tutoring in elementary school language and generated multiple rounds of student-teacher dialogues using ChatGPT-3.5. We obtained a more ideal large-scale language model for essay tutoring in elementary school language by using different datasets and different data input methods.