Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916299904385024 |
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| author | Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David |
| author_facet | Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David |
| contents | 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 |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_17553 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft Kranti, Chalamalasetti Hakimov, Sherzod Schlangen, David Computation and Language 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 |
| title | Retrieval-Augmented Code Generation for Situated Action Generation: A Case Study on Minecraft |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2406.17553 |