On Using Large Language Models to Generate Plans
Abstract: Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Language Models (LLMs) can answer questions, write high-quality programming code, and predict protein folding, showcasing their versatility in solving various tasks beyond language-based problems. This paper explores if and how LLMs can also be used for automated planning given the diverse ways LLMs are modeled and trained. To do so, we seek to answer four key questions. Firstly, we want to understand the effectiveness of different LLM architectures for plan generation. Secondly, we aim to identify which pre-training data (natural language vs code) effectively facilitates plan generation. Thirdly, we investigate whether fine-tuning or prompting is a more effective approach for plan generation. Finally, we explore whether LLMs are capable of plan generalization. By answering these questions, the study seeks to shed light on the capabilities of LLMs in solving complex planning problems and provide insights into the most effective approaches for using LLMs in this context.
Submission Length: Long submission (more than 12 pages of main content)
Assigned Action Editor: ~Hector_Palacios1
Submission Number: 2401
Loading