A wide range of real-world applications is characterized by their symbolic nature, necessitating a strong capability for symbolic reasoning. This paper investigates the potential application of Large Language Models (LLMs) as symbolic reasoners. We focus on text-based games, significant benchmarks for agents with natural language capabilities, particularly in symbolic tasks like math, map reading, sorting, and applying common sense in text-based worlds. To facilitate these agents, we propose an LLM agent designed to tackle symbolic challenges and achieve in-game objectives. We begin by initializing the LLM agent and informing it of its role. The agent then receives observations and a set of valid actions from the text-based games, along with a specific symbolic module. With these inputs, the LLM agent chooses an action and interacts with the game environments. Our experimental results demonstrate that our method significantly enhances the capability of LLMs as automated agents for symbolic reasoning, and our LLM agent is effective in text-based games involving symbolic tasks, achieving an average performance of 88% across all tasks.

翻译：现实世界中广泛的应用具有符号性质，这要求具备强大的符号推理能力。本文研究了大型语言模型（LLMs）作为符号推理器的潜在应用。我们聚焦于基于文本的游戏，这些游戏是评估具备自然语言能力的智能体的重要基准，特别是在数学、地图阅读、排序以及在基于文本的世界中运用常识等符号任务中。为了增强这些智能体的能力，我们提出了一个旨在解决符号挑战并实现游戏目标的大型语言模型智能体。我们首先初始化该智能体并告知其角色，随后智能体从文本游戏中接收观察信息、一组有效动作以及特定的符号模块。基于这些输入，大型语言模型智能体选择动作并与游戏环境进行交互。实验结果表明，我们的方法显著提升了大型语言模型作为自动化符号推理智能体的能力，且在涉及符号任务的文本游戏中表现有效，在所有任务中平均性能达到88%。