Can world knowledge learned by large language models (LLMs) be used to act in
interactive environments? In this paper, we investigate the possibility of
grounding high-level tasks, expressed in natural language (e.g. "make
breakfast"), to a chosen set of actionable steps (e.g. "open fridge"). While
prior work focused on learning from explicit step-by-step examples of how to
act, we surprisingly find that if pre-trained LMs are large enough and prompted
appropriately, they can effectively decompose high-level tasks into mid-level
plans without any further training. However, the plans produced naively by LLMs
often cannot map precisely to admissible actions. We propose a procedure that
conditions on existing demonstrations and semantically translates the plans to
admissible actions. Our evaluation in the recent VirtualHome environment shows
that the resulting method substantially improves executability over the LLM
baseline. The conducted human evaluation reveals a trade-off between
executability and correctness but shows a promising sign towards extracting
actionable knowledge from language models. Website at
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本文研究大型语言模型在互动环境中是否可以利用所学的世界知识来执行高层任务，并提出了一种条件方法，将语言模型生成的中级计划语义上翻译为合适的操作以提高执行性能。在 VirtualHome 环境中的实证评估结果表明，该方法在可执行性方面显著优于大型语言模型基线。