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：我们探讨了与大型语言模型（LLM）的交互如何催生涌现行为，使玩家得以参与游戏叙事的演进。我们的实验环境基于一款文本冒险游戏——玩家需在固定叙事前提下解开谜题，但可自由与由大语言模型GPT-4生成的非玩家角色互动。我们招募了28名玩家进行游戏，并利用GPT-4自动将游戏日志转换为节点图，以表征玩家游戏过程中的叙事结构。研究发现：通过与LLM非确定性行为的交互，玩家能够发现新颖有趣的涌现节点——这些节点并非原始叙事的一部分，却具备成为游戏趣味的潜在价值。创造最多涌现节点的玩家，往往是那些倾向于享受以探索与实验为核心玩法的玩家。