Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons

Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro

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.
Anthology ID:
2024.games-1.7
Volume:
Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Chris Madge, Jon Chamberlain, Karen Fort, Udo Kruschwitz, Stephanie Lukin
Venues:
games | WS
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
61–69
Language:
URL:
https://aclanthology.org/2024.games-1.7
DOI:
Bibkey:
Cite (ACL):
Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, and Giovanni Semeraro. 2024. Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons. In Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024, pages 61–69, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons (Musacchio et al., games-WS 2024)
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PDF:
https://preview.aclanthology.org/dois-2013-emnlp/2024.games-1.7.pdf
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