We explore how interaction with large language models (LLMs) can give rise to emergent behaviors, empowering players to participate in the evolution of game narratives. Our testbed is a text-adventure game in which players attempt to solve a mystery under a fixed narrative premise, but can freely interact with non-player characters generated by GPT-4, a large language model. We recruit 28 gamers to play the game and use GPT-4 to automatically convert the game logs into a node-graph representing the narrative in the player's gameplay. We find that through their interactions with the non-deterministic behavior of the LLM, players are able to discover interesting new emergent nodes that were not a part of the original narrative but have potential for being fun and engaging. Players that created the most emergent nodes tended to be those that often enjoy games that facilitate discovery, exploration and experimentation.

翻译：我们探索了与大型语言模型（LLM）的交互如何引发涌现行为，使玩家能够参与游戏叙事的演进。我们的测试平台是一个文字冒险游戏，玩家在固定叙事前提下去试图解开谜团，但可以自由地与由GPT-4（一种大型语言模型）生成的非玩家角色进行交互。我们招募了28名游戏玩家进行游戏，并使用GPT-4自动将游戏日志转换为节点图，以呈现玩家游戏过程中的叙事。我们发现，通过与LLM非确定性行为的交互，玩家能够发现有趣的新涌现节点，这些节点不属于原始叙事，但具有趣味性和吸引力的潜力。创造最多涌现节点的玩家往往是那些喜欢体验促进发现、探索和实验性游戏的玩家。