In this work, we investigate the capacity of language models to generate explicit, interpretable, and interactive world models of scientific and common-sense reasoning tasks. We operationalize this as a task of generating text games, expressed as hundreds of lines of Python code. To facilitate this task, we introduce ByteSized32 (Code: github.com/cognitiveailab/BYTESIZED32), a corpus of 32 reasoning-focused text games totaling 20k lines of Python code. We empirically demonstrate that GPT-4 can use these games as templates for single-shot in-context learning, successfully producing runnable games on unseen topics in 28% of cases. When allowed to self-reflect on program errors, game runnability substantially increases to 57%. While evaluating simulation fidelity is labor-intensive, we introduce a suite of automated metrics to assess game fidelity, technical validity, adherence to task specifications, and winnability, showing a high degree of agreement with expert human ratings. We pose this as a challenge task to spur further development at the juncture of world modeling and code generation.
@article{arxiv.2305.14879,
title = {ByteSized32: A Corpus and Challenge Task for Generating Task-Specific World Models Expressed as Text Games},
author = {Ruoyao Wang and Graham Todd and Eric Yuan and Ziang Xiao and Marc-Alexandre Côté and Peter Jansen},
journal= {arXiv preprint arXiv:2305.14879},
year = {2023}
}