2 citations · 2 across the 9 of their papers we have counts for
5 papers · 1 filter
Rushes: A Human Preference Dataset for Pluralistic Alignment
Michael Xu, Jorge Leandro, Sudha Rao +5
We introduce Rushes, a dataset and benchmark for studying revealed human engagement preferences in interactive narrative environments. Rushes is collected through a game interface…
GFlowRL: Scaling Distribution-Matching RL to Large Language Models
Xiaodong Liu, Michael Xu, Jack W. Stokes +3
Generative Flow Networks (GFlowNets) offer a promising alternative to reward-maximizing reinforcement learning (RL) for large reasoning models, encouraging diverse reasoning paths…
Collaborative Quest Completion with LLM-driven Non-Player Characters in Minecraft
Sudha Rao, Weijia Xu, Michael Xu +5
The use of generative AI in video game development is on the rise, and as the conversational and other capabilities of large language models continue to improve, we expect LLM-driv…
Player-Driven Emergence in LLM-Driven Game Narrative
Xiangyu Peng, Jessica Quaye, Sudha Rao +10
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…
GENEVA: GENErating and Visualizing branching narratives using LLMs
Jorge Leandro, Sudha Rao, Michael Xu +4
Dialogue-based Role Playing Games (RPGs) require powerful storytelling. The narratives of these may take years to write and typically involve a large creative team. In this work, w…