Large language models (LLMs) such as ChatGPT and GPT-4 have recently demonstrated their remarkable abilities of communicating with human users. In this technical report, we take an initiative to investigate their capacities of playing text games, in which a player has to understand the environment and respond to situations by having dialogues with the game world. Our experiments show that ChatGPT performs competitively compared to all the existing systems but still exhibits a low level of intelligence. Precisely, ChatGPT can not construct the world model by playing the game or even reading the game manual; it may fail to leverage the world knowledge that it already has; it cannot infer the goal of each step as the game progresses. Our results open up new research questions at the intersection of artificial intelligence, machine learning, and natural language processing.
翻译:大型语言模型 (LLM) 如ChatGPT和GPT-4 最近展示了它们与人类用户沟通的卓越能力。 在本技术报告中,我们开始探讨它们在文本游戏中的能力,其中玩家必须通过与游戏世界对话来理解环境并对情况做出反应。 我们的实验表明,与所有现有系统相比,ChatGPT 表现竞争力,但仍然表现水平较低。准确地说,ChatGPT 不能通过玩游戏或甚至阅读游戏手册来构造世界模型;它可能无法利用它已经具备的世界知识;它不能推断游戏进程中每个步骤的目标。我们的研究结果在人工智能、机器学习和自然语言处理的交叉领域引发了新的研究问题。