Grounding Player Intent in Game Commands: A Study of Compact Language Model Adaptation

Abstract

Large language models can interpret user requests plausibly yet fail to produce actions that satisfy structural constraints. We study this problem in Dungeons & Dragons (D&D) combat, where player intent must be grounded in the current game state and translated into well-formed commands for Avrae, a Discord-based D&D automation system. We fine-tune a 4-bit quantized LLaMA-3 8B model with Low-Rank Adaptation on 256 curated FIREBALL-derived examples and compare it with zero-shot, one-shot, and few-shot prompting. On 25 held-out interactions, the model achieves 56% semantic grounding accuracy and 48% strict command validity. Results suggest that parameter-efficient adaptation improves grounding, while formatting, argument, and reference errors remain common.