Knowledge Model Prompting Increases LLM Performance on Planning Tasks

Large Language Models (LLMs) often struggle with reasoning and planning tasks. This paper introduces the Task-Method-Knowledge (TMK) framework, a prompting technique that significantly improves LLM reasoning capabilities. It enables models to better decompose complex planning problems and achieve higher accuracy on symbolic tasks, suggesting a way to bridge the gap between semantic approximation and symbolic manipulation.