Does Reasoning Help LLM Agents Play Dungeons and Dragons? A Prompt Engineering Experiment

This paper explores the application of Large Language Models (LLMs) and reasoning to predict Dungeons & Dragons (DnD) player actions and format them as Avrae Discord bot commands. Using the FIREBALL dataset, we evaluated a reasoning model, DeepSeek-R1-Distill-LLaMA-8B, and an instruct model, LLaMA-3.1-8B-Instruct, for command generation. Our findings highlight the importance of providing specific instructions to models, that even single sentence changes in prompts can greatly affect the output of models, and that instruct models are sufficient for this task compared to reasoning models.

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Publication Details

Journal
Maryland Shared Open Access Repository (USMAI Consortium)
Published
2025-11-09
DOI
https://doi.org/10.13016/m26v97-axw8
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
0.00
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article

Does Reasoning Help LLM Agents Play Dungeons and Dragons? A Prompt Engineering Experiment

Lara J. Martin, Patricia Delafuente, Arya Honraopatil
Maryland Shared Open Access Repository (USMAI Consortium)
Topic Modeling
article

Does Reasoning Help LLM Agents Play Dungeons and Dragons? A Prompt Engineering Experiment

Lara J. Martin, Patricia Delafuente, Arya Honraopatil
article en

Abstract

This paper explores the application of Large Language Models (LLMs) and reasoning to predict Dungeons & Dragons (DnD) player actions and format them as Avrae Discord bot commands. Using the FIREBALL dataset, we evaluated a reasoning model, DeepSeek-R1-Distill-LLaMA-8B, and an instruct model, LLaMA-3.1-8B-Instruct, for command generation. Our findings highlight the importance of providing specific instructions to models, that even single sentence changes in prompts can greatly affect the output of models, and that instruct models are sufficient for this task compared to reasoning models.

Maryland Shared Open Access Repository (USMAI Consortium)
Openalex Percentile: Top 13%
Topic Modeling
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