This survey provides a comprehensive review of research on multi-turn
dialogue systems, with a particular focus on multi-turn dialogue systems based
on large language models (LLMs). This paper aims to (a) give a summary of
existing LLMs and approaches for adapting LLMs to downstream tasks; (b)
elaborate recent advances in multi-turn dialogue systems, covering both
LLM-based open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems,
along with datasets and evaluation metrics; (c) discuss some future emphasis
and recent research problems arising from the development of LLMs and the
increasing demands on multi-turn dialogue systems.

本研究综述了基于大型语言模型 (LLMs) 的多轮对话系统的研究进展，包括适应 LLMs 到下游任务的方法，最近在多轮对话系统中的 LLM-based 开放领域对话 (ODD) 和任务导向对话 (TOD) 系统的进展，并且讨论了 LLMs 的发展和对多轮对话系统需求增加所引起的一些未来重点和最近的研究问题。

基于 LLM 的多轮对话系统的最新进展综述

A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems

Context modeling plays a significant role in building multi-turn dialogue
systems. In order to make full use of context information, systems can use
Incomplete Utterance Rewriting(IUR) methods to simplify the multi-turn dialogue
into single-turn by merging current utterance and context information into a
self-contained utterance. However, previous approaches ignore the intent
consistency between the original query and rewritten query. The detection of
omitted or coreferred locations in the original query can be further improved.
In this paper, we introduce contrastive learning and multi-task learning to
jointly model the problem. Our method benefits from carefully designed
self-supervised objectives, which act as auxiliary tasks to capture semantics
at both sentence-level and token-level. The experiments show that our proposed
model achieves state-of-the-art performance on several public datasets.

该论文介绍了如何使用对比学习和多任务学习相结合的方法来提高上下文建模中的缺失语句重写系统的目的。实验表明，该方法在几个公共数据集上均达到了最先进的性能。