Word2World

This repository contains to code for Word2World: Generating Stories and Worlds through Large Language Models.

Abstract:

Large Language Models (LLMs) have proven their worth across a diverse spectrum of disciplines. LLMs have shown great potential in Procedural Content Generation (PCG) as well, but directly generating a level through a pre-trained LLM is still challenging. This work introduces \texttt{Word2World}, a system that enables LLMs to procedurally design playable games through stories, without any task-specific fine-tuning. \texttt{Word2World} leverages the abilities of LLMs to create diverse content and extract information. Combining these abilities, LLMs can create a story for the game, design narrative, and place tiles in appropriate places to create coherent worlds and playable games. We test \texttt{Word2World} with different LLMs and perform a thorough ablation study to validate each step.

Usage:

Clone the repo:

https://github.com/umair-nasir14/Word2World.git

Install the environment and activate it:

cd Word2World
type > word2world/.env
conda env create -f environment.yml
conda activate word2world

Add your API key to the .env file created in word2world folder:

OPENAI_API_KEY="sk..."

Run with default configs:

python main.py

Or run with specified configs:

python main.py \
--model="gpt-4-turbo-2024-04-09" \
--min_story_paragraphs=4 \
--max_story_paragraphs=5 \
--total_objectives=8 \
--rounds=1 \
--experiment_name="Your_World" \
--save_dir="outputs"

To play the generated game:

python word2world/play_game.py "path_to_game_data\game_data.json"

where game_data.json is generated when the Word2World loop is finished and is saved to \outputs\game_data.json. This can be modified in configs or as --save_dir arg.

To play an example world:

python word2world/play_game.py

Results:

LLM comparison:

Worlds:

Note:

• The most stable model is "gpt-4-turbo-2024-04-09".
• Currently only OpenAI models are supported.

Cite:

@article{nasir2024word2world,
  title={Word2World: Generating Stories and Worlds through Large Language Models},
  author={Nasir, Muhammad U and James, Steven and Togelius, Julian},
  journal={arXiv preprint arXiv:2405.06686},
  year={2024}
}