用对话摘要和角色设定提升狼人杀AI发言一致性
Enhancing Consistency of Werewolf AI through Dialogue Summarization and Persona Information
- 用大模型生成对话摘要，结合人工设计的角色设定增强发言连贯性
- 自匹配对局日志显示，角色语气与立场在整局游戏中保持一致
- 适合关注角色扮演、对话一致性研究的开发者与研究人员
狼人杀是一款依赖玩家推理与讨论能力的沟通类游戏。本文针对2024年AIWolfDial共享任务（与第17届INLG联合举办）开发了一款基于大语言模型的狼人杀AI代理。近年来，像ChatGPT这样的大模型因其出色的生成与推理能力受到广泛关注。本研究通过利用大模型生成的对话摘要，结合人工设计的角色人格设定与话语示例，提升代理发言的一致性。通过对自匹配对局日志的分析，验证了该代理在整局游戏中保持了语境一致性和角色特征，包括语气与立场的稳定。
原文摘要 · Abstract (English)
The Werewolf Game is a communication game where players' reasoning and discussion skills are essential. In this study, we present a Werewolf AI agent developed for the AIWolfDial 2024 shared task, co-hosted with the 17th INLG. In recent years, large language models like ChatGPT have garnered attention for their exceptional response generation and reasoning capabilities. We thus develop the LLM-based agents for the Werewolf Game. This study aims to enhance the consistency of the agent's utterances by utilizing dialogue summaries generated by LLMs and manually designed personas and utterance examples. By analyzing self-match game logs, we demonstrate that the agent's utterances are contextually consistent and that the character, including tone, is maintained throughout the game.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。