FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information
This provides a valuable resource for researchers working on AI in roleplaying games, though it is incremental as it builds on prior work by offering more accurate state data.
The authors tackled the lack of true gold-standard game state data for Dungeons & Dragons by creating FIREBALL, a dataset of nearly 25,000 real gameplay sessions with structured state information, which improved natural language generation quality in automated metrics and human judgments.
Dungeons & Dragons (D&D) is a tabletop roleplaying game with complex natural language interactions between players and hidden state information. Recent work has shown that large language models (LLMs) that have access to state information can generate higher quality game turns than LLMs that use dialog history alone. However, previous work used game state information that was heuristically created and was not a true gold standard game state. We present FIREBALL, a large dataset containing nearly 25,000 unique sessions from real D&D gameplay on Discord with true game state info. We recorded game play sessions of players who used the Avrae bot, which was developed to aid people in playing D&D online, capturing language, game commands and underlying game state information. We demonstrate that FIREBALL can improve natural language generation (NLG) by using Avrae state information, improving both automated metrics and human judgments of quality. Additionally, we show that LLMs can generate executable Avrae commands, particularly after finetuning.