  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.
