Enhancing AI systems with efficient communication skills for effective human assistance necessitates proactive initiatives from the system side to discern specific circumstances and interact aptly. This research focuses on a collective building assignment in the Minecraft dataset, employing language modeling to enhance task understanding through state-of-the-art methods. These models focus on grounding multi-modal understanding and task-oriented dialogue comprehension tasks, providing insights into their interpretative and responsive capabilities. Our experimental results showcase a substantial improvement over existing methods, indicating a promising direction for future research in this domain.

翻译：为赋予人工智能系统高效的沟通能力以有效协助人类，系统需主动识别具体情境并恰当交互。本研究聚焦于Minecraft数据集中的集体建造任务，采用语言建模方法通过前沿技术提升任务理解能力。这些模型专注于多模态理解扎根与任务导向型对话理解任务，揭示了其解释与响应能力的特性。实验结果表明，相较于现有方法取得了显著改进，为这一领域的未来研究指明了方向。