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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.05114 |
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| _version_ | 1866912693395390464 |
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| author | Becker, Álvaro Guglielmin Rossato, Lana Bertoldo Tavares, Anderson Rocha |
| author_facet | Becker, Álvaro Guglielmin Rossato, Lana Bertoldo Tavares, Anderson Rocha |
| contents | Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05114 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Usando LLMs para Programar Jogos de Tabuleiro e Variações Becker, Álvaro Guglielmin Rossato, Lana Bertoldo Tavares, Anderson Rocha Machine Learning Creating programs to represent board games can be a time-consuming task. Large Language Models (LLMs) arise as appealing tools to expedite this process, given their capacity to efficiently generate code from simple contextual information. In this work, we propose a method to test how capable three LLMs (Claude, DeepSeek and ChatGPT) are at creating code for board games, as well as new variants of existing games. |
| title | Usando LLMs para Programar Jogos de Tabuleiro e Variações |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2511.05114 |