LLMR: Real-time Prompting Of Interactive Worlds Using Large Language Models

De La Torre Fernanda, Fang Cathy Mengying, Huang Han, Banburski-fahey Andrzej, Fernandez Judith Amores, Lanier Jaron. Arxiv 2023

[Paper]    
GPT Model Architecture Prompting RAG Reinforcement Learning Tools Training Techniques

We present Large Language Model for Mixed Reality (LLMR), a framework for the real-time creation and modification of interactive Mixed Reality experiences using LLMs. LLMR leverages novel strategies to tackle difficult cases where ideal training data is scarce, or where the design goal requires the synthesis of internal dynamics, intuitive analysis, or advanced interactivity. Our framework relies on text interaction and the Unity game engine. By incorporating techniques for scene understanding, task planning, self-debugging, and memory management, LLMR outperforms the standard GPT-4 by 4x in average error rate. We demonstrate LLMR’s cross-platform interoperability with several example worlds, and evaluate it on a variety of creation and modification tasks to show that it can produce and edit diverse objects, tools, and scenes. Finally, we conducted a usability study (N=11) with a diverse set that revealed participants had positive experiences with the system and would use it again.

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