Enhancing AI systems with efficient communication skills that align with human understanding is crucial for their effective assistance to human users. Proactive initiatives from the system side are needed to discern specific circumstances and interact aptly with users to solve these scenarios. In this research, we opt for a collective building assignment taken from the Minecraft dataset. Our proposed method employs language modeling to enhance task understanding through state-of-the-art (SOTA) methods using language models. These models focus on grounding multi-modal understandinging and task-oriented dialogue comprehension tasks. This focus aids in gaining insights into how well these models interpret and respond to a variety of inputs and tasks. Our experimental results provide compelling evidence of the superiority of our proposed method. This showcases a substantial improvement and points towards a promising direction for future research in this domain.

翻译：增强AI系统与人类理解相一致的高效沟通能力，对于其有效辅助人类用户至关重要。系统方需采取主动措施，以辨别具体情境并与用户进行恰当交互，从而解决这些场景。本研究选取了来自Minecraft数据集的集体建造任务。我们提出的方法采用语言建模，通过使用语言模型的最新最先进（SOTA）方法来增强任务理解能力。这些模型专注于多模态理解基础与面向任务的对话理解任务。这一重点有助于深入了解模型如何解释和响应各种输入与任务。我们的实验结果提供了强有力的证据，证明了所提方法的优越性。这展示了显著的改进，并为此领域的未来研究指明了有前景的方向。