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.
@article{arxiv.2501.03277,
title = {HonkaiChat: Companions from Anime that feel alive!},
author = {Yueze Liu and Yichi Zhang and Shaan Om Patel and Zhaoyang Zhu and Shilong Guo},
journal= {arXiv preprint arXiv:2501.03277},
year = {2025}
}
Comments
5 pages, 4 figures. This is a preprint. Not yet submitted to a journal or conference. More iterated versions to be updated