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

Paper · 2024 · Zihao Yi, Jiarui Ouyang, Zhe Xu et al.

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 UniversityGuangzhou · China23 works · 8,980 citations

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 9,086 worksArtificial Intelligence · 8,889 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 works

🧭 Topics 1Research subjects and languages the work is about.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. DOI 10.48550/arxiv.2402.18013 · arXiv 2402.18013

@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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