Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games

Dekun Wu, Haochen Shi, Zhiyuan Sun, Bang Liu

Abstract
In this study, we explore the application of Large Language Models (LLMs) in Jubensha, a Chinese detective role-playing game and a novel area in Artificial Intelligence (AI) driven gaming. We introduce the first dataset specifically for Jubensha, including character scripts and game rules, to foster AI agent development in this complex narrative environment. Our work also presents a unique multi-agent interaction framework using LLMs, allowing AI agents to autonomously engage in Jubensha games. To evaluate the gaming performance of these AI agents, we developed novel methods measuring their mastery of case information and reasoning skills. Furthermore, we incorporated the latest advancements in prompting engineering to enhance the agents’ performance in information gathering, murderer identification, and logical reasoning. The experimental results validate the effectiveness of our proposed methods. This work aims to offer a novel perspective on understanding LLM capabilities and establish a new benchmark for evaluating large language model-based agents.
Anthology ID:
2024.findings-acl.490
Volume:
Findings of the Association for Computational Linguistics: ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8225–8291
Language:
URL:
https://aclanthology.org/2024.findings-acl.490
DOI:
10.18653/v1/2024.findings-acl.490
Bibkey:
Cite (ACL):
Dekun Wu, Haochen Shi, Zhiyuan Sun, and Bang Liu. 2024. Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games. In Findings of the Association for Computational Linguistics: ACL 2024, pages 8225–8291, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Deciphering Digital Detectives: Understanding LLM Behaviors and Capabilities in Multi-Agent Mystery Games (Wu et al., Findings 2024)
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https://preview.aclanthology.org/dois-2013-emnlp/2024.findings-acl.490.pdf
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