DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
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.
6756a41a8e874bcf2950482e
Minzheng Wang
claimed_verified
2024-12-09T08:30:07.324Z
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mz.w
user
iiiiwis
6756a41a8e874bcf2950482f
Xinghua Zhang
6756a41a8e874bcf29504830
Kun Chen
admin_assigned
2024-12-09T16:02:49.482Z
65f65f0f977559740cb21f08
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KunChen
user
KunChen
6756a41a8e874bcf29504831
Nan Xu
admin_assigned
2024-12-09T16:02:32.548Z
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Xu
user
Nanxu024
6756a41a8e874bcf29504832
Haiyang Yu
6756a41a8e874bcf29504833
Fei Huang
admin_assigned
2024-12-09T16:02:01.998Z
635b8b6a37c6a2c12e2cce00
/avatars/229fb72180529141515d1df797b33709.svg
Fei Huang
user
hzhwcmhf
6756a41a8e874bcf29504834
Wenji Mao
6756a41a8e874bcf29504835
Yongbin Li
admin_assigned
2024-12-09T16:01:33.862Z
66641b2fd8e1e34bc621e688
https://cdn-avatars.huggingface.co/v1/production/uploads/66641b2fd8e1e34bc621e688/csPETwnx2zCIHSWi9uAi-.png
Yongbin Li
user
Yongbin-Li
DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
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.
A new benchmark, DEMO, addresses comprehensive dialogue modeling and evaluation, enhancing dialogue agents through imitation learning.
Dialogue Large language models (LLMs) Element Awareness Dialogue Agent Interaction Imitation learning DEMO benchmark