On the Planning, Search, and Memorization Capabilities of Large Language
  Models
Yunhao Yang Anshul Tomar
The rapid advancement of large language models, such as the Generative
Pre-trained Transformer (GPT) series, has had significant implications across
various disciplines. In this study, we investigate the potential of the
state-of-the-art large language model (GPT-4) for planning tasks. We explore
its effectiveness in multiple planning subfields, highlighting both its
strengths and limitations. Through a comprehensive examination, we identify
areas where large language models excel in solving planning problems and reveal
the constraints that limit their applicability. Our empirical analysis focuses
on GPT-4's performance in planning domain extraction, graph search path
planning, and adversarial planning. We then propose a way of fine-tuning a
domain-specific large language model to improve its Chain of Thought (CoT)
capabilities for the above-mentioned tasks. The results provide valuable
insights into the potential applications of large language models in the
planning domain and pave the way for future research to overcome their
limitations and expand their capabilities.