LLM解复杂序列优化题能力差，用辩证法思想提升效果无需重训练
Are Language Models Up to Sequential Optimization Problems? From Evaluation to a Hegelian-Inspired Enhancement
- 构建动态生成框架WorldGen，可控生成未见序列优化题
- 简单问题表现好，复杂度上升时性能明显下降
- 借鉴黑格尔辩证法提出ACE方法，不需微调即可显著提效
大型语言模型（LLMs）在众多领域展现出惊人能力，为革新优化问题求解——这一关键、普遍且复杂的领域——提供了机遇。本文探讨了LLMs处理序列优化问题（SOPs）的能力。我们提出WorldGen，一个可动态生成具有可控复杂度的未知SOPs的框架，用于评估LLM性能。初步观察发现，尽管LLMs在简单SOPs上表现良好，但随着复杂度增加，其性能显著下降。受此启发，我们重新审视推理的哲学假设以提升LLM表现。受黑格尔辩证法框架的启发，我们提出ACE方法，证明在无需任何重训练或进一步微调的情况下，可显著提升LLMs在SOP场景下的性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。