Knowledge Model Prompting Increases LLM Performance on Planning Tasks

TL;DR

This paper demonstrates that using the Task-Method-Knowledge (TMK) framework as a prompting strategy significantly enhances large language models' reasoning and planning abilities, especially in complex symbolic tasks, surpassing previous methods.

Contribution

It introduces TMK prompting for LLMs, leveraging explicit task decomposition and hierarchical reasoning to improve performance on planning benchmarks like PlanBench.

Findings

Achieved up to 97.3% accuracy on Blocksworld tasks with TMK prompting.

Significant performance improvement over traditional prompting methods.

TMK prompts help models engage formal reasoning pathways instead of linguistic modes.

Abstract

Large Language Models (LLM) can struggle with reasoning ability and planning tasks. Many prompting techniques have been developed to assist with LLM reasoning, notably Chain-of-Thought (CoT); however, these techniques, too, have come under scrutiny as LLMs' ability to reason at all has come into question. Borrowing from the domain of cognitive and educational science, this paper investigates whether the Task-Method-Knowledge (TMK) framework can improve LLM reasoning capabilities beyond its previously demonstrated success in educational applications. The TMK framework's unique ability to capture causal, teleological, and hierarchical reasoning structures, combined with its explicit task decomposition mechanisms, makes it particularly well-suited for addressing language model reasoning deficiencies, and unlike other hierarchical frameworks such as HTN and BDI, TMK provides explicit representations of not just what to do and how to do it, but also why actions are taken. The study evaluates TMK by experimenting on the PlanBench benchmark, focusing on the Blocksworld domain to test for reasoning and planning capabilities, examining whether TMK-structured prompting can help language models better decompose complex planning problems into manageable sub-tasks. Results also highlight significant performance inversion in reasoning models. TMK prompting enables the reasoning model to achieve up to an accuracy of 97.3\% on opaque, symbolic tasks (Random versions of Blocksworld in PlanBench) where it previously failed (31.5\%), suggesting the potential to bridge the gap between semantic approximation and symbolic manipulation. Our findings suggest that TMK functions not merely as context, but also as a mechanism that steers reasoning models away from their default linguistic modes to engage formal, code-execution pathways in the context of the experiments.