Multi Turn Dialogue

Multi-turn dialogue research focuses on enabling large language models (LLMs) to engage in natural, coherent, and contextually relevant conversations spanning multiple turns. Current research emphasizes improving LLM performance in multi-turn settings through techniques like reinforcement learning from human feedback (RLHF), knowledge distillation, and novel masking strategies to optimize both accuracy and efficiency. This area is crucial for advancing human-computer interaction, creating more sophisticated conversational agents for various applications, and developing robust benchmarks for evaluating LLMs' abilities in complex, dynamic dialogues.