DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
Authors: name: Minzheng Wang, name: Xinghua Zhang, name: Kun Chen, name: Nan Xu, name: Haiyang Yu, name: Fei Huang, name: Wenji Mao, name: Yongbin Li

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.