Towards Pedagogical LLMs with Supervised Fine Tuning for Computing Education

Autor:	Vassar, Alexandra, Renzella, Jake, Ross, Emily, Taylor, Andrew
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	This paper investigates supervised fine-tuning of large language models (LLMs) to improve their pedagogical alignment in computing education, addressing concerns that LLMs may hinder learning outcomes. The project utilised a proprietary dataset of 2,500 high quality question/answer pairs from programming course forums, and explores two research questions: the suitability of university course forums in contributing to fine-tuning datasets, and how supervised fine-tuning can improve LLMs' alignment with educational principles such as constructivism. Initial findings suggest benefits in pedagogical alignment of LLMs, with deeper evaluations required. Comment: 3 pages, 1 table, conference
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2411.01765 Zobrazit plný text záznamu View this record from Arxiv