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

2024-02-28

arXiv.org

AI Summary

Existing research on large language model (LLM)-driven multi-turn dialogue systems suffers from fragmented frameworks, heterogeneous technical approaches, and inconsistent evaluation protocols. Method: This paper introduces, for the first time, a unified taxonomy of four LLM adaptation paradigms for multi-turn dialogue—prompt engineering, instruction tuning, retrieval-augmented generation (RAG), and dialogue state modeling—while distinguishing open-domain and task-oriented dialogue along technical and bottleneck dimensions. It constructs a structured technical landscape covering architectures, benchmark datasets, and evaluation metrics, and synthesizes multi-dimensional automatic and human evaluation methodologies. Contribution/Results: The survey identifies three critical research gaps—interpretability, long-horizon consistency, and controllable interaction—and establishes a theoretically grounded, practice-oriented reference framework to guide future work in LLM-based dialogue systems.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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 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.

Problem

• Summarize LLMs and their adaptation to tasks
• Review advances in LLM-based dialogue systems
• Discuss future challenges in multi-turn dialogues

Innovation

• Survey of LLM-based multi-turn dialogue systems
• Review of LLM adaptation for downstream tasks
• Analysis of open-domain and task-oriented dialogues