Game-Time：评估口语语言模型中的时间动态
Game-Time: Evaluating Temporal Dynamics in Spoken Language Models
AI总结：
本文提出Game-Time基准测试，用于评估口语语言模型在时间动态方面的能力，发现现有模型在时间约束下表现不佳，揭示了时间感知和全双工交互的不足。
摘要：
对话式口语语言模型（SLMs）正成为实时语音交互的有前途的范式。然而，其时间动态能力，包括管理时间、节奏和同时说话的能力，仍然是对话流畅性的重要且未评估的挑战。为解决这一差距，我们引入了Game-Time基准测试，一个系统评估这些时间能力的框架。受人类通过语言活动学习语言的启发，Game-Time包含基本的指令跟随任务和具有时间约束的高级任务，如节奏遵守和同步响应。对多种SLM架构的评估揭示了明显的表现差异：虽然最先进的模型能处理基本任务，但许多现代系统仍难以处理基本的指令跟随。更关键的是，几乎所有模型在时间约束下都会显著退化，暴露了持续的时间感知和全双工交互的弱点。Game-Time基准测试为未来研究提供了指导，以开发更具时间意识的对话AI。演示和数据集可在我们的项目网站上获得：https://ga642381.github.io/Game-Time。
英文摘要：
Conversational Spoken Language Models (SLMs) are emerging as a promising paradigm for real-time speech interaction. However, their capacity of temporal dynamics, including the ability to manage timing, tempo and simultaneous speaking, remains a critical and unevaluated challenge for conversational fluency. To address this gap, we introduce the Game-Time Benchmark, a framework to systematically assess these temporal capabilities. Inspired by how humans learn a language through language activities, Game-Time consists of basic instruction-following tasks and advanced tasks with temporal constraints, such as tempo adherence and synchronized responses. Our evaluation of diverse SLM architectures reveals a clear performance disparity: while state-of-the-art models handle basic tasks well, many contemporary systems still struggle with fundamental instruction-following. More critically, nearly all models degrade substantially under temporal constraints, exposing persistent weaknesses in time awareness and full-duplex interaction. The Game-Time Benchmark provides a foundation for guiding future research toward more temporally-aware conversational AI. Demos and datasets are available on our project website https://ga642381.github.io/Game-Time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
