AI 中文总结
研究虚拟现实中大型语言模型辅助几何编辑的意图消歧问题，通过结合澄清问题与空间锚定图形预览的混合方法，经实验验证其能提升交互稳定性、改善用户体验，为相关系统集成提供设计依据。
AI 中文摘要
用户意图消歧在智能交互系统中仍是关键挑战。在二维界面的对话系统中已广泛研究，但在沉浸式环境中大型语言模型辅助编辑工作流程里的意图消歧研究有限。本文评估如何用空间锚定图形预览增强传统基于对话的消歧，以解决大型语言模型辅助参数驱动编辑工作流程中模糊的用户命令。24名参与者在虚拟现实模拟场景中完成复杂几何编辑任务的受试者内研究表明，结合澄清问题和图形预览的混合方法能以更少对话轮次支持更好的交互稳定性并改善用户体验，为大型语言模型辅助参数驱动沉浸式场景编辑中的消歧方法有效性提供了实证证据，并为未来大型语言模型在先进虚拟现实/增强现实系统中的集成提供设计指南。
英文摘要
User intent disambiguation remains a key challenge in intelligent interactive systems. While they have been widely studied in dialogue systems in 2D interfaces, research on how intent disambiguation could be incorporated within Large Language Model (LLM) assisted editing workflows in immersive environments remains limited. Recent advances in LLMs create opportunities to leverage the immersive nature of virtual and augmented reality (VR/AR) environments to provide better disambiguation support. In this paper, we evaluate how traditional dialogue-based disambiguation can be augmented with spatially-anchored graphical previews to resolve ambiguous user commands in LLM-assisted parameter-driven editing workflows. A within-subjects study in which 24 participants completed complex geometry editing tasks in VR simulate scenarios where VR scenes are controlled by numerical parameters. Compared with the condition where disambiguation is not available, quantitative metrics and qualitative feedback indicate that a hybrid approach which combines clarification questions and graphical previews can support better interaction stability with fewer conversation rounds while improving user experience. These findings provide empirical evidence on the effectiveness of disambiguation methods in LLM-assisted editing of parameter-driven immersive scenes and inform design guidelines for future integration of LLMs in advanced VR/AR systems.
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