---
title: "A Survey on Recent Advances in LLM-Based Multi-turn Dialogue"
description: "This creates problems that single-turn systems avoid: long-range dependencies, consistency across… 181 references, cited by 70 papers here."
authors: ["Zihao Yi", "Jiarui Ouyang", "Zhe Xu", "Yuwen Liu", "Tianhao Liao", "Haohao Luo", "Ying Shen"]
year: 2024
venue: "arXiv:2402.18013"
source: "https://arxiv.org/abs/2402.18013"
doi: "10.48550/arxiv.2402.18013"
arxiv: "2402.18013"
licence: "CC0 1.0 (arXiv)"
licence_url: "http://creativecommons.org/publicdomain/zero/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/yi-2024-survey-recent-advances-llm-based/"
---
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# A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems

**Authors:** Zihao Yi, Jiarui Ouyang, Zhe Xu, Yuwen Liu, Tianhao Liao, Haohao Luo and [Ying Shen](https://md.research.elamur.ai/free/en/authors/ying-shen/) · **Year:** 2024 · **Venue:** [arXiv:2402.18013](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/) ([ACM Computing Surveys 2024](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/2024/)) · **Publisher:** [Association for Computing Machinery](https://md.research.elamur.ai/free/en/publishers/association-for-computing-machinery/) · **Licence:** [CC0 1.0 (arXiv)](http://creativecommons.org/publicdomain/zero/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2402.18013) · [PDF](https://arxiv.org/pdf/2402.18013) · [DOI 10.48550/arxiv.2402.18013](https://doi.org/10.48550/arxiv.2402.18013) · [arXiv 2402.18013](https://arxiv.org/abs/2402.18013) · [OpenAlex](https://openalex.org/W4415689913)

**Citations:** 59 citations (OpenAlex, 2026-10-14) · 70 in our library, of the 141,840 papers whose bibliographies we read

## Summary
Multi-turn dialogue systems must track earlier turns, not only the latest message. This creates problems that single-turn systems avoid: long-range dependencies, consistency across turns, and vague or partial user input. Older rule-based, retrieval-based and neural systems handled these poorly and needed expensive domain-specific engineering. This survey from Sun Yat-Sen University examines how large language models change the field.

It first groups LLMs by Transformer layout and reviews ways to adapt them to dialogue, covering several fine-tuning variants and prompting techniques. It then organises the literature into two families. Task-oriented dialogue (TOD) is divided into modular pipelines and end-to-end models. Open-domain dialogue (ODD) is divided into retrieval, generation and hybrid approaches. The survey also reviews evaluation metrics and datasets.

Being a survey, it reports no new experiments. Its value is a single taxonomy spanning both dialogue types, plus a set of open research problems. This makes it a practical starting point for teams building LLM-based conversational assistants.

## Key points
- **Model taxonomy:** decoder-only models (GPT, LLaMA, GLM series), encoder-only models (BERT, UNILM) and encoder-decoder models (BART, T5). Decoder-only models are further split into causal and prefix decoders.
- **Adaptation methods:** full fine-tuning, parameter-efficient fine-tuning and ways to reduce fine-tuning instability, plus prompt engineering via ICL, CoT and RAG.
- **TOD coverage:**
  - Pipeline modules NLU, DST, PL and NLG, with examples such as SUMBT, IC-DST, LDST, GDP-Zero and SC-GPT.
  - End-to-end systems such as Soloist, SimpleTOD, UBAR, PPTOD and SPACE.
- **ODD coverage:**
  - Retrieval-based: MSN, IoI Network, Self-RAG.
  - Generation-based: PLATO-LTM, PAML.
  - Hybrid: Retro, FiD, K2R, Chain-of-Note.
- **Open problems:** coherence over long contexts, robust domain adaptation, tool-calling agents (with ChatDev and MetaGPT as examples), and privacy risks from prompt injection and prompt leaking.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Sun Yat-sen University](https://md.research.elamur.ai/free/en/universities/sun-yat-sen-university/) — University, Guangzhou, China · 23 works · 8,980 citations

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🧭 Topics (1): [🗨️ Dialogue systems](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · 401 works

## Cite

Cite as: Zihao Yi, Jiarui Ouyang, Zhe Xu, Yuwen Liu, Tianhao Liao, Haohao Luo and Ying Shen. 2024. A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems. arXiv:2402.18013.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{yi2024survey,
  title = {{A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems}},
  author = {Zihao Yi, Jiarui Ouyang, Zhe Xu, Yuwen Liu, Tianhao Liao, Haohao Luo and Ying Shen},
  year = {2024},
  doi = {10.48550/arxiv.2402.18013},
  eprint = {2402.18013},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2402.18013}
}
```

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*Updated 24 October 2026*