Enhance Reasoning for Large Language Models in the Game Werewolf
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914733946306560 |
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| author | Wu, Shuang Zhu, Liwen Yang, Tao Xu, Shiwei Fu, Qiang Wei, Yang Fu, Haobo |
| author_facet | Wu, Shuang Zhu, Liwen Yang, Tao Xu, Shiwei Fu, Qiang Wei, Yang Fu, Haobo |
| contents | This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. Unlike augmenting LLMs with prompt engineering, Thinker directly harnesses knowledge from databases and employs various optimization techniques. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks such as natural language processing, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge. Our framework is presented using a 9-player Werewolf game that demands dual-system reasoning. We introduce a communication protocol between LLMs and the Thinker, and train the Thinker using data from 18800 human sessions and reinforcement learning. Experiments demonstrate the framework's effectiveness in deductive reasoning, speech generation, and online game evaluation. Additionally, we fine-tune a 6B LLM to surpass GPT4 when integrated with the Thinker. This paper also contributes the largest dataset for social deduction games to date. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_02330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Enhance Reasoning for Large Language Models in the Game Werewolf Wu, Shuang Zhu, Liwen Yang, Tao Xu, Shiwei Fu, Qiang Wei, Yang Fu, Haobo Artificial Intelligence Computation and Language This paper presents an innovative framework that integrates Large Language Models (LLMs) with an external Thinker module to enhance the reasoning capabilities of LLM-based agents. Unlike augmenting LLMs with prompt engineering, Thinker directly harnesses knowledge from databases and employs various optimization techniques. The framework forms a reasoning hierarchy where LLMs handle intuitive System-1 tasks such as natural language processing, while the Thinker focuses on cognitive System-2 tasks that require complex logical analysis and domain-specific knowledge. Our framework is presented using a 9-player Werewolf game that demands dual-system reasoning. We introduce a communication protocol between LLMs and the Thinker, and train the Thinker using data from 18800 human sessions and reinforcement learning. Experiments demonstrate the framework's effectiveness in deductive reasoning, speech generation, and online game evaluation. Additionally, we fine-tune a 6B LLM to surpass GPT4 when integrated with the Thinker. This paper also contributes the largest dataset for social deduction games to date. |
| title | Enhance Reasoning for Large Language Models in the Game Werewolf |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2402.02330 |