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Large language models (LLMs), such as ChatGPT, have demonstrated outstandingperformance in various fields, particularly in natural language understandingand generation tasks. In complex application scenarios, users tend to engage inmulti-turn conversations with ChatGPT to keep contextual information and obtaincomprehensive responses. However, human forgetting and model contextualforgetting remain prominent issues in multi-turn conversation scenarios, whichchallenge the users' conversation comprehension and contextual continuity forChatGPT. To address these challenges, we propose an interactive conversationvisualization system called C5, which includes Global View, Topic View, andContext-associated Q\&A View. The Global View uses the GitLog diagram metaphorto represent the conversation structure, presenting the trend of conversationevolution and supporting the exploration of locally salient features. The TopicView is designed to display all the question and answer nodes and theirrelationships within a topic using the structure of a knowledge graph, therebydisplay the relevance and evolution of conversations. The Context-associatedQ\&A View consists of three linked views, which allow users to exploreindividual conversations deeply while providing specific contextual informationwhen posing questions. The usefulness and effectiveness of C5 were evaluatedthrough a case study and a user study.

C5: Towards Better Conversation Comprehension and Contextual Continuity for ChatGPT