Turn-based conversation models are essential for creating AI systems that can engage in natural and coherent dialogues with users. Our research in this area focuses on enhancing the turn-taking capabilities of the LLaMA3 model, ensuring that it can maintain the flow of conversation effectively.

Our approach includes:

• Dialogue State Tracking: Developing mechanisms to keep track of the conversation state, enabling the model to understand the context of each turn and respond appropriately.
• Conversational Memory: Implementing memory modules that allow the model to recall previous interactions, maintaining context and continuity over extended dialogues.
• Adaptive Response Generation: Creating models that can adapt their responses based on the dynamics of the conversation, ensuring that the AI remains relevant and engaging throughout the interaction.