语言模型时代增量对话与机器人动作管理的既往经验
Prior Lessons of Incremental Dialogue and Robot Action Management for the Age of Language Models
AI总结：
本文回顾了单词级或更低级别的增量交互系统文献，指出现有大型语言模型因处理单调性而缺乏增量修正能力，重点调研了对话管理器的增量建模，并为大型语言模型时代的具身机器人平台提出了实用增量对话管理的要求。
摘要：
赋予机器人说话能力的努力受益于自然语言处理的最新进展，特别是大型语言模型。然而，当前的语言模型并非完全增量的，因为其处理本质上是单调的，因此缺乏根据最新观察修正其解释或输出的能力。这种单调性对用于人机交互的对话系统开发具有重要影响。在本文中，我们回顾了以增量方式（即在单词级别或更低级别）运行的交互式系统的相关文献。我们论证了对增量系统的需求，调研了对话重要方面的增量建模，如语音识别和语言生成。主要关注点在于系统中负责决策的部分，即对话管理器。我们发现关于增量对话管理的研究非常少，提出了实用增量对话管理的一些要求，以及大型语言模型时代增量对话对具身机器人平台的影响。
英文摘要：
Efforts towards endowing robots with the ability to speak have benefited from recent advancements in natural language processing, in particular large language models. However, current language models are not fully incremental, as their processing is inherently monotonic and thus lack the ability to revise their interpretations or output in light of newer observations. This monotonicity has important implications for the development of dialogue systems for human--robot interaction. In this paper, we review the literature on interactive systems that operate incrementally (i.e., at the word level or below it). We motivate the need for incremental systems, survey incremental modeling of important aspects of dialogue like speech recognition and language generation. Primary focus is on the part of the system that makes decisions, known as the dialogue manager. We find that there is very little research on incremental dialogue management, offer some requirements for practical incremental dialogue management, and the implications of incremental dialogue for embodied, robotic platforms in the age of large language models.
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。
