Conversational modeling using Large Language Models (LLMs) requires a nuanced understanding of context to generate coherent and contextually relevant responses. In this paper, we present Token Trails, a novel approach that leverages token-type embeddings to navigate the intricate contextual nuances within conversations. Our framework utilizes token-type embeddings to distinguish between user utterances and bot responses, facilitating the generation of context-aware replies. Through comprehensive experimentation and evaluation, we demonstrate the effectiveness of Token Trails in improving conversational understanding and response generation, achieving state-of-the-art performance. Our results highlight the significance of contextual modeling in conversational AI and underscore the promising potential of Token Trails to advance the field, paving the way for more sophisticated and contextually aware chatbot interactions.

通过利用令牌类型嵌入来区分用户话语和机器人回答，Token Trails是一种新颖的方法，用于在对话中导航复杂的上下文细微差别，提高对话理解和回复生成的性能，展示了上下文建模在对话人工智能中的重要性，并凸显Token Trails在推动该领域发展方面的潜力，为更复杂和上下文感知的聊天机器人互动铺平了道路。

Token Trails: 导航对话AI中的上下文深度