A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models

• URL: http://arxiv.org/abs/2311.16789v2
• Date: Sun, 20 Jul 2025 10:06:23 GMT
• Title: A Survey of the Evolution of Language Model-Based Dialogue Systems: Data, Task and Models
• Authors: Hongru Wang, Lingzhi Wang, Yiming Du, Liang Chen, Jingyan Zhou, Yufei Wang, Kam-Fai Wong,
• Abstract summary: We take a deep look at the history of the dialogue system, especially its special relationship with the advancements of language models.<n>This survey delves into the dynamic interplay between language models and dialogue systems, unraveling the evolutionary path of this essential relationship.
• Score: 24.120097746860928
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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