--- license: cc-by-4.0 task_categories: - text-generation - text2text-generation language: - en tags: - story - storytelling - story generation - dnd - creative generation - command generation - dungeons and dragons - ttrpg - dungeon master pretty_name: FIREBALL configs: - config_name: default data_files: - split: filtered path: "filtered/*.jsonl.gz" dataset_info: features: - name: combat_id dtype: string - name: event_type dtype: string - name: timestamp dtype: float - name: message_id dtype: string - name: author_id dtype: string - name: author_name dtype: string - name: created_at dtype: float - name: content dtype: string - name: embeds dtype: list - name: proxy_url dtype: string - name: fields dtype: list - name: components dtype: list language_creators: - crowdsourced --- # Dataset Card for FIREBALL ## Table of Contents - [Data Description](#data-description) - [Filtered Triplets Schema](#filtered-triplets-schema) - [Normalized Actor State](#normalized-actor-state) - [Additional Information](#additional-information) - [Citation](#citation) - [Licensing](#licensing) --- ## Data Description **FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information** FIREBALL is a large crowdsourced dataset of people playing Dungeons and Dragons on Discord. In addition to playing the game using natural language (primarily English), players also used a bot called [Avrae](https://avrae.io/). Avrae enables players to keep track of the state of the game by writing commands, which we collected. The data contains nearly 25,000 unique sessions of gameplay. * [Published paper](https://aclanthology.org/2023.acl-long.229/) * [Paper on arXiv](https://arxiv.org/abs/2305.01528) **Abstract** > 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. ### Filtered Triplets Schema All user IDs and usernames have been randomized (by way of a hash function) to preserve anonymity. Each line contains a filtered triple, each of which includes the following keys: ``` { "speaker_id": The anonymized user ID of the user who sent the commands in the triple. "before_utterances": A list of strings corresponding to the "preceding" utterances in the triple. "combat_state_before": A list of normalized actor states (see below) for each actor in the combat instance at the instant before the command was run. "current_actor": (nullable) The normalized actor state of the actor whose turn it currently is. "commands_norm": A list of strings corresponding to the "commands" portion of the triple. "automation_results": A mechanically generated list of strings representing the results of running the action in the Avrae engine. "caster_after": The normalized actor state of the actor who ran the action(s), which may or may not be the current actor. "targets_after": A list of normalized actor states for each actor who was targeted by the action. "combat_state_after": A list of normalized actor states for each actor in the combat instance at the instant after the command was run. "after_utterances": A list of strings corresponding to the "following" utterances in the triple. "utterance_history": The last 5 messages in the chat history before the command was run. "before_idxs": A list of integers corresponding to the index of the "message" events containing the "preceding" utterances in the raw event file. "before_state_idx": The index of the "combat_state_update" event in the raw event file that was used to derive "combat_state_before". "command_idxs": The indexes of the "command" events corresponding to the "commands_norm" key. "after_state_idx": The index of the "combat_state_update" event corresponding to the "combat_state_after" key. "after_idxs": The indexes of the "message" events corresponding to the "after_utterances" key. "embed_idxs": (nullable, same length as "automation_results") The indexes of "message" events corresponding to rich results shown to players on Discord for each result in the "automation_results" key. } ``` #### Normalized Actor State The normalized actor state is only a subset of the available actor information, corresponding to the information we used for our engineering experiments for the FIREBALL paper. For a full list of available actor information, see table 6 in the [FIREBALL paper](https://aclanthology.org/2023.acl-long.229/). ``` { "name": The name of the actor. "hp": The numerical and narrative hit points (e.g. "<12/34; Bloodied>"). "class": The actor's class(es) and level(s), if applicable (e.g. "Fighter 3") "race": The actor's race, if applicable (e.g. "Mountain Dwarf", "Adult Red Dragon"). "attacks": A list of the actor's available attack names. "spells": A list of the actor's available spells. "actions": A list of the actor's available special abilities. "effects": A list of any temporary effects on the actor (e.g. "Stunned"). "description": The actor's narrative description (if available). "controller_id": The anonymized user ID of this actor's controller. } ``` ## Additional Information ### Citation ``` @inproceedings{Zhu2023FIREBALL, title={{FIREBALL: A Dataset of Dungeons and Dragons Actual-Play with Structured Game State Information}}, author={Zhu, Andrew and Aggarwal, Karmanya and Feng, Alexander and Martin, Lara J. and Callison-Burch, Chris}, year={2023}, booktitle={Annual Meeting of the Association for Computational Linguistics (ACL)}, month={7}, url={https://aclanthology.org/2023.acl-long.229/}, address={Toronto, Canada}, pages={4171--4193}, publisher={ACL}, doi={10.18653/v1/2023.acl-long.229} } ``` --- ### Licensing The Creative Commons Attribution 4.0 International License. https://creativecommons.org/licenses/by/4.0/