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

在该研究中，我们调查了使用大型语言模型（LLMs）来预测建造者采取的动作序列，在 Minecraft 协作建筑任务中，建筑师通过指示建造者使用 3D 方块来组装指定结构。借助 LLMs 的上下文学习能力，我们使用少样本启发式技术显著提高了性能，并对性能差距进行了详细分析，以作为未来工作的参考。

检索增强的代码生成用于情境动作生成：Minecraft 案例研究

Retrieval-Augmented Code Generation for Situated Action Generation: A  Case Study on Minecraft

Various human activities can be abstracted into a sequence of actions in
natural text, i.e. cooking, repairing, manufacturing, etc. Such action
sequences heavily depend on the executing order, while disorder in action
sequences leads to failure of further task execution by robots or AI agents.
Therefore, to verify the order reasoning capability of current neural models in
sequential tasks, we propose a challenging benchmark , named STEPS. STEPS
involves two subtask settings, focusing on determining the rationality of given
next step in recipes and selecting the reasonable step from the multi-choice
question, respectively. We describe the data construction and task
formulations, and benchmark most of significant Large Language Models (LLMs).
The experimental results demonstrate 1) The commonsense reasoning of action
orders in sequential tasks are challenging to resolve via zero-shot prompting
or few-shot in-context learning for LLMs; 2) Prompting method still
significantly lags behind tuning-based method on STEPS.

提出了一个基于序列任务的具有挑战性的基准测试 STEPS，它包括两个子任务设置，聚焦于确定食谱中给定下一步的合理性和从多项选择题中选择合理步骤，通过实验结果说明了序列任务中的常识推理具有挑战性，并且提示方法在 STEPS 上仍然明显落后于基于调优的方法。