Large language models (LLMs), such as ChatGPT, have demonstrated outstanding performance in various fields, particularly in natural language understanding and generation tasks. In complex application scenarios, users tend to engage in multi-turn conversations with ChatGPT to keep contextual information and obtain comprehensive responses. However, human forgetting and model contextual forgetting remain prominent issues in multi-turn conversation scenarios, which challenge the users' conversation comprehension and contextual continuity for ChatGPT. To address these challenges, we propose an interactive conversation visualization system called C5, which includes Global View, Topic View, and Context-associated Q\&A View. The Global View uses the GitLog diagram metaphor to represent the conversation structure, presenting the trend of conversation evolution and supporting the exploration of locally salient features. The Topic View is designed to display all the question and answer nodes and their relationships within a topic using the structure of a knowledge graph, thereby display the relevance and evolution of conversations. The Context-associated Q\&A View consists of three linked views, which allow users to explore individual conversations deeply while providing specific contextual information when posing questions. The usefulness and effectiveness of C5 were evaluated through a case study and a user study.

翻译：大型语言模型（LLMs），如ChatGPT，在自然语言理解与生成等各类任务中展现出卓越性能。在复杂应用场景中，用户倾向于与ChatGPT进行多轮对话以保持上下文信息并获得全面回复。然而，人类遗忘与模型上下文遗忘仍是多轮对话场景中的突出问题，这影响了用户对ChatGPT的对话理解与上下文连贯性。针对上述挑战，我们提出一个名为C5的交互式对话可视化系统，包含全局视图、主题视图和上下文关联问答视图。全局视图采用GitLog图隐喻表征对话结构，呈现对话演化趋势并支持局部显著特征探索；主题视图以知识图谱结构展示主题内所有问答节点及其关联关系，从而呈现对话的相关性与演化过程；上下文关联问答视图由三个关联子视图组成，允许用户深入探索单个对话，并在提问时提供特定上下文信息。通过案例研究与用户实验评估了C5系统的实用性与有效性。