  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.
