Summary of A Survey on Recent Advances in Llm-based Multi-turn Dialogue Systems, by Zihao Yi et al.
A Survey on Recent Advances in LLM-Based Multi-turn Dialogue Systems
by Zihao Yi, Jiarui Ouyang, Yuwen Liu, Tianhao Liao, Zhe Xu, Ying Shen
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty Summary: This survey paper provides a comprehensive overview of research on multi-turn dialogue systems, particularly those based on large language models (LLMs). The paper summarizes existing LLMs and approaches for adapting them to downstream tasks. It also covers recent advances in open-domain dialogue (ODD) and task-oriented dialogue (TOD) systems, including datasets and evaluation metrics like perplexity and success rate. Additionally, the paper discusses future research directions and challenges arising from the development of LLMs and increasing demands on multi-turn dialogue systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty Summary: This paper looks at how computers can have conversations with us. It focuses on ways to make computers better at talking back and forth in multiple turns. The paper talks about large language models, which are computer programs that can understand and generate human-like text. It also covers different types of conversations, like chatting about general topics or doing specific tasks together. The paper discusses what’s been learned so far and what still needs to be figured out. |
Keywords
» Artificial intelligence » Perplexity
