Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs
Claire Jin, Sudha Rao, Xiangyu Peng, Portia Botchway, Jessica Quaye, Chris Brockett, Bill Dolan
Abstract
Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPCs). However, LLMs may exhibit flaws such as hallucinations, forgetfulness, or misinterpretations of prompts, causing logical inconsistencies and unexpected deviations from intended designs. Automated techniques for detecting such game bugs are still lacking. To address this, we propose a systematic LLM-based method for automatically identifying such bugs from player game logs, eliminating the need for collecting additional data such as post-play surveys. Applied to a text-based game DejaBoom!, our approach effectively identifies bugs inherent in LLM-powered interactive games, surpassing unstructured LLM-powered bug-catching methods and filling the gap in automated detection of logical and design flaws.
BibTeX
@inproceedings{jin-etal-2024-automatic,
title = "Automatic Bug Detection in {LLM}-Powered Text-Based Games Using {LLM}s",
author = "Jin, Claire and
Rao, Sudha and
Peng, Xiangyu and
Botchway, Portia and
Quaye, Jessica and
Brockett, Chris and
Dolan, Bill",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.907/",
doi = "10.18653/v1/2024.findings-acl.907",
pages = "15353--15368"
}