Integrating Large Language Models with Digital Avatars: A Case Study of the Educational Domain
Yongle Du, Bohan Zhou, Yiming Wang, Jinghui Liu, Linqian Gong, Yuyan Zhu, Jianing Sun · 2024
In recent years, the integration of Large Language Models (LLMs) with digital avatars has gained significant attention, particularly in educational and customer service domains. This paper presents a comprehensive case study of applying LLMs to enhance the intelligence and interaction capabilities of digital avatars. By fine-tuning LLMs with localized knowledge bases, the study aims to improve domain-specific responsiveness and real-time interaction in educational settings. Additionally, the integration of advanced Text-to-Speech (TTS) and Speech-to-Text (STT) technologies further enhances the ability to interact naturally with users. Theoretical analysis delves into the self-attention mechanisms of LLMs and their interaction with knowledge bases, while empirical results validate the improvements in user experience and response accuracy. By addressing the challenges of latency, response accuracy, and emotional expressiveness, this study offers valuable insights for the future development of multimodal digital avatars and their applications in education.