Token Trails: Navigating Contextual Depths in Conversational AI with
  ChatLLM
Md. Kowsher Ritesh Panditi Nusrat Jahan Prottasha Prakash Bhat Anupam Kumar Bairagi Mohammad Shamsul Arefin
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.