  In the Minecraft Collaborative Building Task, two players collaborate: an
Architect (A) provides instructions to a Builder (B) to assemble a specified
structure using 3D blocks. In this work, we investigate the use of large
language models (LLMs) to predict the sequence of actions taken by the Builder.
Leveraging LLMs' in-context learning abilities, we use few-shot prompting
techniques, that significantly improve performance over baseline methods.
Additionally, we present a detailed analysis of the gaps in performance for
future work
