HonkaiChat: Companions from Anime that feel alive!
This work addresses the need for more lifelike and engaging chatbots, particularly for anime-themed role-playing and interactive dialogue, though it appears incremental as it builds on existing conversational agent methods.
The paper tackled the problem of reactive and personality-driven chatbots lacking dynamic interactions by proposing an event-driven dialogue framework, which improved conversational engagement and naturalness while reducing hallucinations in evaluations on GPT-4 and industry baselines.
Modern conversational agents, including anime-themed chatbots, are frequently reactive and personality-driven but fail to capture the dynamic nature of human interactions. We propose an event-driven dialogue framework to address these limitations by embedding dynamic events in conversation prompts and fine-tuning models on character-specific data. Evaluations on GPT-4 and comparisons with industry-leading baselines demonstrate that event-driven prompts significantly improve conversational engagement and naturalness while reducing hallucinations. This paper explores the application of this approach in creating lifelike chatbot interactions within the context of Honkai: Star Rail, showcasing the potential for dynamic event-based systems to transform role-playing and interactive dialogue.