DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling

2024-12-06

arXiv.org

AI Summary

Existing LLM-based dialogue systems lack systematic modeling of dialogue stages, leading to incomplete benchmarks and coarse-grained evaluation—hindering precise generation and assessment. Method: We propose Dialogue Element Modeling (DEMO), a novel task that first partitions the dialogue lifecycle into three phases—prelude, dialogue exchange, and epilogue—and formally defines fine-grained dialogue elements and their coupling relationships. We introduce a dual subtask framework—dialogue element awareness and agent interaction—and design a DEMO agent via imitation learning, incorporating structured phase annotation, element-decoupled modeling, and cross-domain generalization training. Contribution/Results: We establish the first comprehensive, lifecycle-spanning benchmark and evaluation framework supporting multi-element coordination. Experiments reveal substantial deficiencies of mainstream LLMs in element identification and generation; our DEMO agent significantly outperforms baselines in element modeling fidelity, generation quality, and cross-domain generalization.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Conversational AI/Dialog Systems
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information

Abstract

Large language models (LLMs) enabled dialogue systems have become one of the central modes in human-machine interaction, which bring about vast amounts of conversation logs and increasing demand for dialogue generation. The dialogue's life-cycle spans from $ extit{Prelude}$ through $ extit{Interlocution}$ to $ extit{Epilogue}$, encompassing rich dialogue elements. Despite large volumes of dialogue-related studies, there is a lack of systematic investigation into the dialogue stages to frame benchmark construction that covers comprehensive dialogue elements. This hinders the precise modeling, generation and assessment of LLMs-based dialogue systems. To bridge this gap, in this paper, we introduce a new research task--$ extbf{D}$ialogue $ extbf{E}$lement $ extbf{MO}$deling, including $ extit{Element Awareness}$ and $ extit{Dialogue Agent Interaction}$, and propose a novel benchmark, $ extbf{DEMO}$, designed for a comprehensive dialogue modeling and assessment. On this basis, we further build the DEMO agent with the adept ability to model dialogue elements via imitation learning. Extensive experiments on DEMO indicate that current representative LLMs still have considerable potential for enhancement, and our DEMO agent performs well in both dialogue element modeling and out-of-domain tasks.

Problem

• Systematic study of dialogue stages
• Comprehensive dialogue elements modeling
• Enhancement of LLMs-based dialogue systems

Innovation

• Fine-grained dialogue element modeling
• Imitation learning for DEMO agent
• Comprehensive dialogue modeling benchmark