@inproceedings{musacchio-etal-2024-leveraging,
    title = "Leveraging Large Language Models for Spell-Generation in Dungeons {\&} Dragons",
    author = "Musacchio, Elio  and
      Siciliani, Lucia  and
      Basile, Pierpaolo  and
      Semeraro, Giovanni",
    editor = "Madge, Chris  and
      Chamberlain, Jon  and
      Fort, Karen  and
      Kruschwitz, Udo  and
      Lukin, Stephanie",
    booktitle = "Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.games-1.7",
    pages = "61--69",
    abstract = "Dungeons {\&} Dragons (D{\&}D) is a classic tabletop game with a 50-year history. Its intricate and customizable gameplay allows players to create endless worlds and stories. Due to the highly narrative component of this game, D{\&}D and many other interactive games represent a challenging setting for the Natural Language Generation (NLG) capabilities of LLMs. This paper explores using LLMs to generate new spells, which are one of the most captivating aspects of D{\&}D gameplay. Due to the scarcity of resources available for such a specific task, we build a dataset of 3,259 instances by combining official and fan-made D{\&}D spells. We considered several LLMs in generating spells, which underwent a quantitative and qualitative evaluation. Metrics including Bleu and BertScore were computed for quantitative assessments. Subsequently, we also conducted an in-vivo evaluation with a survey involving D{\&}D players, which could assess the quality of the generated spells as well as their adherence to the rules. Furthermore, the paper emphasizes the open-sourcing of all models, datasets, and findings, aiming to catalyze further research on this topic.",
}

Dungeons {\&} Dragons (D{\&}D) is a classic tabletop game with a 50-year history. Its intricate and customizable gameplay allows players to create endless worlds and stories. Due to the highly narrative component of this game, D{\&}D and many other interactive games represent a challenging setting for the Natural Language Generation (NLG) capabilities of LLMs. This paper explores using LLMs to generate new spells, which are one of the most captivating aspects of D{\&}D gameplay. Due to the scarcity of resources available for such a specific task, we build a dataset of 3,259 instances by combining official and fan-made D{\&}D spells. We considered several LLMs in generating spells, which underwent a quantitative and qualitative evaluation. Metrics including Bleu and BertScore were computed for quantitative assessments. Subsequently, we also conducted an in-vivo evaluation with a survey involving D{\&}D players, which could assess the quality of the generated spells as well as their adherence to the rules. Furthermore, the paper emphasizes the open-sourcing of all models, datasets, and findings, aiming to catalyze further research on this topic.
Musacchio, Elio Siciliani, Lucia Basile, Pierpaolo Semeraro, Giovanni
Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons