@inproceedings{ch-etal-2024-retrieval,
    title = "Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on {M}inecraft",
    author = "Kranti, Chalamalasetti  and
      Hakimov, Sherzod  and
      Schlangen, David",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2024.findings-emnlp.652/",
    doi = "10.18653/v1/2024.findings-emnlp.652",
    pages = "11159--11170",
    abstract = "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."
}