@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.",
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="musacchio-etal-2024-leveraging">
<titleInfo>
<title>Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons</title>
</titleInfo>
<name type="personal">
<namePart type="given">Elio</namePart>
<namePart type="family">Musacchio</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Lucia</namePart>
<namePart type="family">Siciliani</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Pierpaolo</namePart>
<namePart type="family">Basile</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Giovanni</namePart>
<namePart type="family">Semeraro</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2024-05</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024</title>
</titleInfo>
<name type="personal">
<namePart type="given">Chris</namePart>
<namePart type="family">Madge</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Jon</namePart>
<namePart type="family">Chamberlain</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Karen</namePart>
<namePart type="family">Fort</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Udo</namePart>
<namePart type="family">Kruschwitz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Stephanie</namePart>
<namePart type="family">Lukin</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>ELRA and ICCL</publisher>
<place>
<placeTerm type="text">Torino, Italia</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<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.</abstract>
<identifier type="citekey">musacchio-etal-2024-leveraging</identifier>
<location>
<url>https://aclanthology.org/2024.games-1.7</url>
</location>
<part>
<date>2024-05</date>
<extent unit="page">
<start>61</start>
<end>69</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons
%A Musacchio, Elio
%A Siciliani, Lucia
%A Basile, Pierpaolo
%A Semeraro, Giovanni
%Y Madge, Chris
%Y Chamberlain, Jon
%Y Fort, Karen
%Y Kruschwitz, Udo
%Y Lukin, Stephanie
%S Proceedings of the 10th Workshop on Games and Natural Language Processing @ LREC-COLING 2024
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F musacchio-etal-2024-leveraging
%X 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.
%U https://aclanthology.org/2024.games-1.7
%P 61-69
Markdown (Informal)
[Leveraging Large Language Models for Spell-Generation in Dungeons & Dragons](https://aclanthology.org/2024.games-1.7) (Musacchio et al., games-WS 2024)
ACL