DEMO : Reframing Dialogue Interaction with Fine-grained Element Modeling (2025)

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 \textit{Prelude} through \textit{Interlocution} to \textit{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— \textbf{D} ialogue \textbf{E} lement \textbf{MO} deling, including \textit{Element Awareness} and \textit{Dialogue Agent Interaction} , and propose a novel benchmark, \textbf{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.

## introduction under the compelling drive of large language models (llms), the development of intelligent language interfaces is undergoing an unprecedented transformation, with llms-empowered dialogue systems emerging as one of the central modes in human-machine interaction @xcite @xcite . the relentless evolution of llms has penetrated increasingly complex interaction environments, necessitating an enhancement of expressive intelligence @xcite @xcite and a sharp sensitivity to the pivotal elements within interactions @xcite . by meticulously analyzing vast conversation logs, we can gain valuable insights into the critical elements underlying dialogue interaction, such as persona, scenario, and interaction goal, which are vital for enhancing the modeling, generation, and assessment of humanmachine interaction systems. typically, a conversational dialogue is conducted with a goal-oriented focus, relying upon a profound understanding of its core elements @xcite @xcite . dialogue agents navigate towards their goals within the constraints of the scene, utilizing conversation strategy to interact with other participants, ultimately producing conversational content with communicative intent. in reality, the dialogue's lifecycle spans from the prelude through the interlocution to the epilogue, encompassing a variety of key elements @xcite ## related work we review related research based on two trends in interactive dialogue systems: (1) dialogue analysis: inferring dialogue elements based on dialogue content. (2) dialogue generation: generating dialogue content according to the dialogue settings. while we discuss related work from the perspectives of two trends mentioned above, different types of dialogue systems and their distinction are discussed in appendix b.2. ## conclusion in this work, we redefine the dialogue framework and introduce a new research task: dialogue element modeling. this task involves two fundamental capabilities, element awareness and dialogue agent interaction, which enhance the complex modeling and comprehensive evaluation of dialogue systems. we first develop a process for constructing dialogue elements and create the benchmark, demo, which includes comprehensive dialogue elements suitable for both english and chinese. we further develop the demo agent specifically for dialogue element modeling. through extensive experiments, we assess the performance of several advanced llms, revealing that there is still room for improvement in this task. in addition, the results demonstrate that our demo agent delivers excellent performance in both in-domain and outof-domain scenarios.