A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
Zihao Yi, Jiarui Ouyang, Zhe Xu, Yuwen Liu, Tianhao Liao, Haohao Luo, Ying Shen
Abstract
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 article aims to (a) giving 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.
Topics & Concepts
Computer scienceFocus (optics)Engineering ethicsData scienceManagement scienceKnowledge managementDownstream (manufacturing)On LanguageDevelopment (topology)Open researchSpeech and dialogue systemsTopic ModelingNeurobiology of Language and Bilingualism