LLMs for Coding and Robotics Education
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866913228860162048 |
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| author | Shu, Peng Zhao, Huaqin Jiang, Hanqi Li, Yiwei Xu, Shaochen Pan, Yi Wu, Zihao Liu, Zhengliang Lu, Guoyu Guan, Le Chen, Gong Liu, Xianqiao Wang Tianming |
| author_facet | Shu, Peng Zhao, Huaqin Jiang, Hanqi Li, Yiwei Xu, Shaochen Pan, Yi Wu, Zihao Liu, Zhengliang Lu, Guoyu Guan, Le Chen, Gong Liu, Xianqiao Wang Tianming |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06116 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LLMs for Coding and Robotics Education Shu, Peng Zhao, Huaqin Jiang, Hanqi Li, Yiwei Xu, Shaochen Pan, Yi Wu, Zihao Liu, Zhengliang Lu, Guoyu Guan, Le Chen, Gong Liu, Xianqiao Wang Tianming Robotics Artificial Intelligence 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. |
| title | LLMs for Coding and Robotics Education |
| topic | Robotics Artificial Intelligence |
| url | https://arxiv.org/abs/2402.06116 |