Understanding the Capabilities of Large Language Models for Automated Planning

• URL: http://arxiv.org/abs/2305.16151v1
• Date: Thu, 25 May 2023 15:21:09 GMT
• Title: Understanding the Capabilities of Large Language Models for Automated Planning
• Authors: Vishal Pallagani and Bharath Muppasani and Keerthiram Murugesan and Francesca Rossi and Biplav Srivastava and Lior Horesh and Francesco Fabiano and Andrea Loreggia
• Abstract summary: The study seeks to shed light on the capabilities of LLMs in solving complex planning problems. It provides insights into the most effective approaches for using LLMs in this context.
• Score: 24.37599752610625
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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. In this paper, we aim to explore how LLMs can also be used for automated planning. To do so, we seek to answer four key questions. Firstly, we want to understand the extent to which LLMs can be used for plan generation. Secondly, we aim to identify which pre-training data is most effective in facilitating 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.

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