Large Language Models (LLMs) are revolutionizing how we interact with machines, but they face a constant challenge: maintaining high-quality responses while keeping latency low, especially in multi-turn dialogues. Existing LLMs, broadly categorized as causal or prefix LLMs, each have their limitations. Causal LLMs, like those behind ChatGPT, offer faster responses but can struggle with context as conversations get longer. Prefix LLMs, on the other hand, excel at using historical context, producing richer interactions, but suffer from slower response times in extended dialogues. Researchers have introduced a new masking paradigm called the Intermittent Semi-working Mask (ISM) to address these issues. The core innovation lies in how ISM handles attention, the mechanism by which LLMs weigh the importance of different parts of a conversation. Instead of consistently applying one type of attention (unidirectional for causal, bidirectional for prefix), ISM strategically alternates between the two. For the question turns of the conversation, it uses bidirectional attention, grasping the nuances of what’s being asked, much like a prefix LLM. For the answer turns, however, it switches to unidirectional attention, focusing on generating coherent and contextually appropriate responses without excessive processing, similar to the speedier causal LLM. This blended approach delivers a best-of-both-worlds scenario. The ISM method not only keeps the quality of prefix LLM high but also achieves the lower latency of causal LLMs, a win-win situation. In benchmark tests, LLMs enhanced with ISM outperformed standard models in generating high-quality answers and demonstrating significantly faster response times. These gains are particularly prominent in longer conversations. While current research focuses on incorporating ISM into existing LLMs through fine-tuning, future explorations will investigate integrating it directly into the initial training process, potentially leading to even more substantial gains in LLM efficiency and conversational abilities. ISM's potential to revolutionize LLM efficiency is apparent. This technique holds the key to empowering LLMs to become nimble conversationalists, maintaining both their profound contextual awareness and their speed of response, vital ingredients for truly engaging and interactive AI.