Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents

Understand

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.