Large Language Models (LLMs) have demonstrated impressive capabilities across numerous fields, presenting an opportunity to revolutionize optimization problem-solving, a crucial, ubiquitous, and complex domain. This paper explores the proficiency of LLMs in handling Sequential Optimization Problems (SOPs). We introduce WorldGen, a dynamic framework for generating unseen SOPs with controllable complexities, to evaluate LLM performance. Our initial observations reveal that while LLMs perform well on simple SOPs, their performance significantly degrades with increased complexity. Motivated by this, we revisit philosophical hypotheses on reasoning to enhance LLM performance. Inspired by the influential framework of Hegelian Dialectics, we propose ACE, demonstrating how the performance of LLMs in SOP contexts can be significantly improved without any retraining or further fine-tuning.

本文探讨大型语言模型（LLMs）在处理顺序优化问题（SOPs）方面的能力，发现在简单SOPs上表现良好，但在复杂性增加时性能显著下降。为此，提出了一种动态框架WorldGen用于生成可控复杂度的SOPs，及基于黑格尔辩证法的ACE方法，表明无须重新训练或微调即可显著提升LLMs在SOP上下文中的表现。

语言模型能否解决顺序优化问题？从评估到黑格尔启发的增强