[3Win5] Poster session 3

[3Win5-11] Discrepancy Between Humans and LVLMs in Directed Graph Annotations of Learning Sequences: A Case Study on the Middle School Mathematics Curriculum

〇Yongmin Kim¹, Takeshi Kojima¹, Yusuke Iwasawa¹, Yutaka Matsuo¹ (1.University of Tokyo)

Keywords:Large Vision and Language Model, Curriculum Learning, Curriculum Graph

In machine learning, curriculum learning is one of the methods that enables models to learn more effectively. To implement curriculum learning, it is necessary to determine the learning sequence. However, deciding this sequence requires annotating the dependencies between data and performing this task manually is costly. This study investigates whether Large Vision-Language Models (LVLMs) can create learning curricula in place of humans. Specifically, we evaluated whether LVLMs can generate curricula based on individual units from the middle school mathematics curriculum guidelines, in a manner similar to humans. Both humans and LVLMs were presented with explanations and example problems from two different units of the curriculum guidelines and were asked to identify the learning dependencies, based on which a curriculum was constructed. The results showed that humans tended to identify relationships between tasks more effectively than LVLMs. These findings suggest that in order to use existing LVLMs to examine task dependencies and create specific curricula, it is necessary to enhance the alignment of LVLMs with humans further.