Daily Arxiv

This is a page that curates AI-related papers published worldwide.
All content here is summarized using Google Gemini and operated on a non-profit basis.
Copyright for each paper belongs to the authors and their institutions; please make sure to credit the source when sharing.

Verbal Werewolf: Engage Users with Verbalized Agentic Werewolf Game Framework

Created by
  • Haebom

Author

Qihui Fan, Wenbo Li, Enfu Nan, Yixiao Chen, Lei Lu, Pu Zhao, Yanzhi Wang

Outline

This paper highlights the growing need for an intelligent framework for human-AI collaboration in social reasoning games, particularly Werewolf. While previous studies have demonstrated that LLMs outperform humans in Werewolf, they point out latency issues due to their reliance on external modules and their limited academic scope. Therefore, in this paper, we propose "Verbal Werewolf," a novel Werewolf game system that leverages state-of-the-art LLMs and a fine-tuned TTS module to enable near-real-time gameplay. By leveraging the enhanced inference capabilities of LLMs, such as DeepSeek V3, without the need for external decision-making modules, we aim to deliver a more immersive and human-like gaming experience that significantly increases user engagement compared to existing text-based frameworks.

Takeaways, Limitations

Takeaways:
We present the possibility of implementing a social inference game system utilizing LLMs at near real-time speed without relying on external modules.
Leveraging the enhanced reasoning capabilities of LLMs to deliver more immersive and human-like gaming experiences.
Improve user engagement by vocalizing text output through the TTS module.
Demonstrates the practical applicability of LLM in the field of social reasoning games.
Limitations:
Dependence on specific LLMs, such as DeepSeek V3. Further research is needed to compare performance and generalize to other LLMs.
Further improvements are needed to the performance and naturalness of the TTS module.
Large-scale user testing is needed to verify user experience and game balance.
Further research is needed on its scalability to various social inference games.
👍