基于语言模型的对话系统演进综述：数据、任务与模型
A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
AI总结：
本文按语言模型突破的时间顺序系统综述对话系统演进，梳理数据、任务与模型变化，并讨论开放挑战与未来方向。
摘要：
对话系统（DS），包括任务导向对话系统（TOD）和开放域对话系统（ODD），一直是自然语言处理（NLP）中的基础任务，在实践中支持各种应用。由于精心的训练和良好设计的模型架构，语言模型（LM）通常被用作构建对话系统的必要骨干。因此，语言模型的每一次突破都会带来对话系统学习范式和研究重点的转变，尤其是预训练语言模型（PLMs）和大型语言模型（LLMs）的出现。在本文中，我们深入审视对话系统的历史，特别是其与语言模型进展之间的特殊关系。因此，本综述提供了一个系统性的视角，按照与语言模型突破相一致的时间顺序对不同阶段进行分类，全面回顾了最先进的研究成果。此外，我们将注意力转向新兴主题，并对开放挑战展开讨论，为基于LLM的对话系统的未来方向提供了有价值的见解。总之，本综述深入探讨了语言模型与对话系统之间的动态相互作用，揭示了这一重要关系的演化路径。通过这一探索，我们为该领域更深入的理解铺平了道路，指导基于LM的对话系统的未来发展。
英文摘要：
Dialogue systems (DS), including the task-oriented dialogue system (TOD) and the open-domain dialogue system (ODD), have always been a fundamental task in natural language processing (NLP), allowing various applications in practice. Owing to sophisticated training and well-designed model architecture, language models (LM) are usually adopted as the necessary backbone to build the dialogue system. Consequently, every breakthrough in LM brings about a shift in learning paradigm and research attention within dialogue system, especially the appearance of pre-trained language models (PLMs) and large language models (LLMs). In this paper, we take a deep look at the history of the dialogue system, especially its special relationship with the advancements of language models. Thus, our survey offers a systematic perspective, categorizing different stages in a chronological order aligned with LM breakthroughs, providing a comprehensive review of state-of-the-art research outcomes. What's more, we turn our attention to emerging topics and engage in a discussion on open challenges, providing valuable insights into the future directions for LLM-based dialogue systems. In summary, this survey delves into the dynamic interplay between language models and dialogue systems, unraveling the evolutionary path of this essential relationship. Through this exploration, we pave the way for a deeper comprehension of the field, guiding future developments in LM-based dialogue systems.
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。
