Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft
<|reference_start|>Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft: 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<|reference_end|>
@article{kranti2024retrieval-augmented,
    title={Retrieval-Augmented Code Generation for Situated Action Generation: A
  Case Study on Minecraft},
    author={Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen},
    journal={arXiv preprint arXiv:2406.17553},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.17553},
    primaryClass={cs.CL}
}