Recent advancements in prompting techniques for Large Language Models (LLMs) have improved their reasoning, planning, and action abilities. This paper examines these prompting techniques through the lens of model predictive control (MPC). We show that LLMs act as implicit planning cost function minimizers when planning prompts are used. Under our framework we demonstrate that LLM planning performance can be improved further by incorporating real planning cost functions and evaluators.

翻译：近年来，大语言模型（LLMs）提示技术的进步显著提升了其在推理、规划与执行方面的能力。本文从模型预测控制（MPC）的视角审视这些提示技术。我们证明，当使用规划提示时，LLMs 充当了隐式规划代价函数的最小化器。在此框架下，我们通过引入真实的规划代价函数与评估器，进一步展示了提升 LLM 规划性能的可能性。