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

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arxiv 2402.18013 v2 pith:7ERAR5JA submitted 2024-02-28 cs.CL cs.AI

Zihao Yi , Jiarui Ouyang , Zhe Xu , Yuwen Liu , Tianhao Liao , Haohao Luo , Ying Shen This is my paper
classification cs.CLcs.AI
keywords dialoguesystemsmulti-turnllmsrecentadvancesllm-basedresearch
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 8 Pith papers

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