EMNLP 2024finding2 citations
Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft
Chalamalasetti Kranti, Sherzod Hakimov, David Schlangen
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.
BibTeX
@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://aclanthology.org/2024.findings-emnlp.652/",
doi = "10.18653/v1/2024.findings-emnlp.652",
pages = "11159--11170"
}