用图模型分析对话流，让聊天机器人对话更清晰可读
A Computational Approach to Modeling Conversational Systems: Analyzing Large-Scale Quasi-Patterned Dialogue Flows
- 构建对话图谱，用过滤重连法去除噪声、保留语义结构
- 相比旧方法，语义指标提升2.06倍，对话结构呈树状且无扭曲
- 适合研究聊天机器人、对话系统优化和用户行为分析
随着基于大语言模型的对话系统兴起，对话动态分析日益重要。本文提出一种新型计算框架，用于构建捕捉松散对话流结构的对话图谱，称为准模式对话。我们引入过滤重连方法，一种新型图简化技术，在最小化噪声的同时保持语义连贯性和结构完整性。对比分析表明，结合大语言模型与该图简化技术后，语义度量S相比以往方法提升了2.06倍，同时实现0 δ-超双曲性的树状结构，确保对话建模的最优清晰性。本工作提供了一种分析大规模对话数据集的计算方法，具有监控聊天机器人、对话管理工具及用户行为分析等实际应用价值。
原文摘要 · Abstract (English)
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.
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