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.
@article{arxiv.2402.05440,
title = {Improving Agent Interactions in Virtual Environments with Language Models},
author = {Jack Zhang},
journal= {arXiv preprint arXiv:2402.05440},
year = {2024}
}