title: DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
summary: Large language models (LLMs) have made dialogue one of the central modes of
  human-machine interaction, leading to the accumulation of vast amounts of
  conversation logs and increasing demand for dialogue generation. A
  conversational life-cycle spans from the Prelude through the Interlocution to
  the Epilogue, encompassing various elements. Despite the existence of numerous
  dialogue-related studies, there is a lack of benchmarks that encompass
  comprehensive dialogue elements, hindering precise modeling and systematic
  evaluation. To bridge this gap, we introduce an innovative research task
  Dialogue Element MOdeling, including
  Element Awareness and Dialogue Agent Interaction, and
  propose a novel benchmark, DEMO, designed for a comprehensive
  dialogue modeling and assessment. Inspired by imitation learning, we further
  build the agent which possesses the adept ability to model dialogue elements
  based on the DEMO benchmark. Extensive experiments indicate that existing LLMs
  still exhibit considerable potential for enhancement, and our DEMO agent has
  superior performance in both in-domain and out-of-domain tasks.
paper_authors._id: 6756a41a8e874bcf2950482e
paper_authors.name: Minzheng Wang
paper_authors.status: claimed_verified
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paper_authors.fullname: mz.w
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paper_authors.user: iiiiwis
paper_authors._id: 6756a41a8e874bcf2950482f
paper_authors.name: Xinghua Zhang
paper_authors._id: 6756a41a8e874bcf29504830
paper_authors.name: Kun Chen
paper_authors.status: admin_assigned
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paper_authors.fullname: KunChen
paper_authors.type: user
paper_authors.user: KunChen
paper_authors._id: 6756a41a8e874bcf29504831
paper_authors.name: Nan Xu
paper_authors.status: admin_assigned
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paper_authors.fullname: Xu
paper_authors.type: user
paper_authors.user: Nanxu024
paper_authors._id: 6756a41a8e874bcf29504832
paper_authors.name: Haiyang Yu
paper_authors._id: 6756a41a8e874bcf29504833
paper_authors.name: Fei Huang
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-09T16:02:01.998Z
paper_authors._id: 635b8b6a37c6a2c12e2cce00
paper_authors.avatarUrl: /avatars/229fb72180529141515d1df797b33709.svg
paper_authors.fullname: Fei Huang
paper_authors.type: user
paper_authors.user: hzhwcmhf
paper_authors._id: 6756a41a8e874bcf29504834
paper_authors.name: Wenji Mao
paper_authors._id: 6756a41a8e874bcf29504835
paper_authors.name: Yongbin Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-12-09T16:01:33.862Z
paper_authors._id: 66641b2fd8e1e34bc621e688
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
paper_authors.fullname: Yongbin Li
paper_authors.type: user
paper_authors.user: Yongbin-Li
paper_title: DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
paper_summary: Large language models (LLMs) have made dialogue one of the central modes of
  human-machine interaction, leading to the accumulation of vast amounts of
  conversation logs and increasing demand for dialogue generation. A
  conversational life-cycle spans from the Prelude through the Interlocution to
  the Epilogue, encompassing various elements. Despite the existence of numerous
  dialogue-related studies, there is a lack of benchmarks that encompass
  comprehensive dialogue elements, hindering precise modeling and systematic
  evaluation. To bridge this gap, we introduce an innovative research task
  Dialogue Element MOdeling, including
  Element Awareness and Dialogue Agent Interaction, and
  propose a novel benchmark, DEMO, designed for a comprehensive
  dialogue modeling and assessment. Inspired by imitation learning, we further
  build the agent which possesses the adept ability to model dialogue elements
  based on the DEMO benchmark. Extensive experiments indicate that existing LLMs
  still exhibit considerable potential for enhancement, and our DEMO agent has
  superior performance in both in-domain and out-of-domain tasks.
paper_ai_keywords: Dialogue Element Modeling Element Awareness Dialogue Agent Interaction DEMO benchmark imitation learning LLMs (Large Language Models) dialogue elements in-domain out-of-domain tasks