コーディングとロボティクス教育のための大規模言語モデル
LLMs for Coding and Robotics Education
ロボットプログラミング教育における大規模言語モデルの活用を検討し、主要モデルを従来のコーディングとブロック図を含むロボットコード生成で評価した。GPT-4Vが全テストで優位だったが、ブロック図画像の生成は苦手だった。
著者: Peng Shu, Huaqin Zhao, Hanqi Jiang, Yiwei Li, Shaochen Xu, Yi Pan, Zihao Wu, Zhengliang Liu, Guoyu Lu, Le Guan, Gong Chen, Xianqiao Wang Tianming Liu
分類: cs.RO, cs.AI
原文アブストラクト
Large language models and multimodal large language models have revolutionized artificial intelligence recently. An increasing number of regions are now embracing these advanced technologies. Within this context, robot coding education is garnering increasing attention. To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification. In this paper, we highlight an important trend in robot coding education. We test several mainstream large language models on both traditional coding tasks and the more challenging task of robot code generation, which includes block diagrams. Our results show that GPT-4V outperforms other models in all of our tests but struggles with generating block diagram images.
