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

October 20, 2025 ยท Declared Dead ยท ๐Ÿ› arXiv.org

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Authors Patricia Delafuente, Arya Honraopatil, Lara J. Martin arXiv ID 2510.18112 Category cs.CL: Computation & Language Citations 0 Venue arXiv.org Last Checked 6 months ago
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.
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