Recent advancements in natural language processing, particularly with large language models (LLMs) like GPT-4, have significantly enhanced dialogue systems, enabling them to generate more natural and fluent conversations. Despite these improvements, challenges persist, such as managing continuous dialogues, memory retention, and minimizing hallucinations. The AIWolfDial2024 addresses these challenges by employing the Werewolf Game, an incomplete information game, to test the capabilities of LLMs in complex interactive environments. This paper introduces a LLM-based Werewolf Game AI, where each role is supported by situation analysis to aid response generation. Additionally, for the werewolf role, various persuasion strategies, including logical appeal, credibility appeal, and emotional appeal, are employed to effectively persuade other players to align with its actions.

翻译：近年来，自然语言处理领域，特别是以GPT-4为代表的大语言模型取得了显著进展，极大地提升了对话系统的能力，使其能够生成更自然、流畅的对话。尽管取得了这些进步，但仍存在一些挑战，例如管理连续对话、保持记忆一致性以及减少幻觉生成。AIWolfDial2024通过采用狼人杀这一不完全信息博弈游戏，来测试大语言模型在复杂交互环境中的能力。本文介绍了一种基于大语言模型的狼人杀游戏AI，其中每个角色都通过情境分析来辅助生成回应。此外，对于狼人角色，我们采用了多种说服策略，包括逻辑诉求、可信度诉求和情感诉求，以有效说服其他玩家与其行动保持一致。