一种对话系统建模的计算方法：分析大规模准模式化对话流
A Computational Approach to Modeling Conversational Systems: Analyzing Large-Scale Quasi-Patterned Dialogue Flows
AI总结：
本文提出一种基于大语言模型和Filter & Reconnect图简化技术的对话图建模方法，用于分析准模式化对话流，使语义指标S提升2.06倍并实现0 δ-双曲率的树状结构，可应用于聊天机器人监控和用户行为分析。
摘要：
随着基于大语言模型的系统在不同场景中与用户交互的兴起，对话动态分析变得越来越重要。本文提出了一种新颖的计算框架，用于构建对话图，以捕捉组织松散的对话（称为准模式化对话）的流程和结构。我们引入了Filter & Reconnect方法，这是一种新颖的图简化技术，在保留对话图语义连贯性和结构完整性的同时最小化噪声。通过对比分析，我们证明将大语言模型与我们的图简化技术相结合，使语义指标S相比先前方法提高了2.06倍，同时强制实现了0 δ-双曲率的树状结构，确保对话建模具有最佳清晰度。这项工作为分析大规模对话数据集提供了一种计算方法，在监控自动化系统（如聊天机器人、对话管理工具和用户行为分析）方面具有实际应用价值。
英文摘要：
The analysis of conversational dynamics has gained increasing importance with the rise of large language model-based systems, which interact with users across diverse contexts. In this work, we propose a novel computational framework for constructing conversational graphs that capture the flow and structure of loosely organized dialogues, referred to as quasi-patterned conversations. We introduce the Filter & Reconnect method, a novel graph simplification technique that minimizes noise while preserving semantic coherence and structural integrity of conversational graphs. Through comparative analysis, we demonstrate that the use of large language models combined with our graph simplification technique has resulted in semantic metric S increasing by a factor of 2.06 compared to previous approaches while simultaneously enforcing a tree-like structure with 0 δ-hyperbolicity, ensuring optimal clarity in conversation modeling. This work provides a computational method for analyzing large-scale dialogue datasets, with practical applications related to monitoring automated systems such as chatbots, dialogue management tools, and user behavior analytics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
