DEMO: Reframing Dialogue Interaction with Fine-grained Element Modeling
Minzheng Wang, Xinghua Zhang, Kun Chen, Nan Xu, Haiyang Yu, Fei Huang, Wenji Mao, Yongbin Li
2024-12-09
Summary
This paper talks about DEMO, a new framework designed to improve how AI models understand and generate conversation by breaking down dialogue into smaller, detailed elements.
What's the problem?
While large language models (LLMs) have become popular for generating dialogue, they often lack a systematic way to evaluate and model conversations. Existing studies do not provide comprehensive benchmarks that cover all the different parts of a dialogue, making it hard to create AI that can interact naturally and effectively.
What's the solution?
The authors introduce a new research task called Dialogue Element Modeling, which focuses on two main areas: Element Awareness (understanding the components of a conversation) and Dialogue Agent Interaction (how well the AI can interact in a goal-directed way). They developed the DEMO benchmark to assess these abilities, allowing for a more detailed evaluation of dialogue systems. By using this framework, they created an agent that can learn from examples and model dialogue elements effectively. Their experiments show that this new approach significantly improves performance in generating and understanding dialogue.
Why it matters?
This research is important because it enhances the way AI systems can engage in conversations. By providing a detailed framework for analyzing dialogue, DEMO helps create more natural and effective AI interactions. This has implications not just for chatbots but also for improving communication in various fields like customer service, education, and social interaction.
Abstract
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.