演示：利用细粒度元素建模重新构建对话交互

DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling

December 6, 2024

作者: Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li

cs.AI

摘要

大型语言模型（LLMs）已经使对话成为人机交互的核心模式之一，导致大量对话日志的积累，并增加了对对话生成的需求。对话生命周期从序幕经过交际到结语，涵盖了各种要素。尽管存在许多与对话相关的研究，但缺乏涵盖全面对话要素的基准，阻碍了精确建模和系统评估。为弥补这一差距，我们引入了一项创新的研究任务——对话要素建模，包括要素意识和对话代理交互，并提出了一个新颖的基准，DEMO，旨在进行全面的对话建模和评估。受模仿学习启发，我们进一步构建了代理，具有模拟对话要素的熟练能力，基于DEMO基准。大量实验表明，现有的LLMs仍然具有相当大的增强潜力，而我们的DEMO代理在领域内外任务中表现出优越性能。

English

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.