@inproceedings{ch-etal-2024-retrieval,
title = "Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on {M}inecraft",
author = "Ch, Kranti 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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft
%A Ch, Kranti
%A Hakimov, Sherzod
%A Schlangen, David
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Findings of the Association for Computational Linguistics: EMNLP 2024
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F ch-etal-2024-retrieval
%X 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.
%U https://aclanthology.org/2024.findings-emnlp.652
%P 11159-11170
Markdown (Informal)
[Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft](https://aclanthology.org/2024.findings-emnlp.652) (Ch et al., Findings 2024)
ACL