README.md

---
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/