Dialogue systems, including task-oriented_dialogue_system (TOD) and open-domain_dialogue_system (ODD), have undergone significant transformations, with language_models (LM) playing a central role. This survey delves into the historical trajectory of dialogue systems, elucidating their intricate relationship with advancements in language models by categorizing this evolution into four distinct stages, each marked by pivotal LM breakthroughs: 1) Early_Stage: characterized by statistical LMs, resulting in rule-based or machine-learning-driven dialogue_systems; 2) Independent development of TOD and ODD based on neural_language_models (NLM; e.g., LSTM and GRU), since NLMs lack intrinsic knowledge in their parameters; 3) fusion between different types of dialogue systems with the advert of pre-trained_language_models (PLMs), starting from the fusion between four_sub-tasks_within_TOD, and then TOD_with_ODD; and 4) current LLM-based_dialogue_system, wherein LLMs can be used to conduct TOD and ODD seamlessly. Thus, our survey provides a chronological perspective aligned with LM breakthroughs, offering a comprehensive review of state-of-the-art research outcomes. What's more, we focus on emerging topics and discuss open challenges, providing valuable insights into future directions for LLM-based_dialogue_systems. Through this exploration, we pave the way for a deeper_comprehension of the evolution, guiding future developments in LM-based dialogue_systems.

翻译：对话系统，包括面向任务的对话系统（TOD）和开放域对话系统（ODD），在语言模型（LM）发挥核心作用的情况下经历了显著变革。本综述深入追溯了对话系统的历史轨迹，通过将其演变划分为四个不同阶段，阐明了它们与语言模型进展之间的复杂关联，每个阶段都以关键的语言模型突破为标志：1）早期阶段：以统计语言模型为特征，产生了基于规则或机器学习驱动的对话系统；2）基于神经语言模型（NLM；如LSTM和GRU）的TOD与ODD独立发展阶段，由于NLM参数中缺乏内在知识；3）随着预训练语言模型（PLMs）的出现，不同类型对话系统之间的融合，始于TOD内部四个子任务的融合，进而到TOD与ODD的融合；4）当前基于大语言模型（LLM）的对话系统阶段，其中LLM可无缝用于执行TOD和ODD。因此，本综述提供了与语言模型突破相一致的时序视角，对最新研究成果进行了全面回顾。此外，我们聚焦新兴主题并探讨开放挑战，为基于LLM的对话系统的未来发展方向提供了宝贵见解。通过这一探索，我们为深入理解其演变铺平了道路，指导了基于LM的对话系统的未来发展。