将优化模型与大语言模型协同，提升智能决策效果
SOLID: a Framework of Synergizing Optimization and LLMs for Intelligent Decision-Making
- 通过价格与偏差惩罚实现优化与语言模型的迭代协作
- 在投资组合案例中实现收敛，年化收益优于纯优化方法
- 适合需要隐私保护与智能推理的自动化决策场景
本文提出SOLID（Synergizing Optimization and Large Language Models for Intelligent Decision-Making），一种将数学优化与大语言模型（LLMs）上下文能力融合的新框架。SOLID通过双重价格和偏差惩罚机制，促进优化与LLM代理间的迭代协作，提升决策质量，同时保持模块化与数据隐私性。在凸性假设下，框架具备理论收敛性，为提示设计提供指导。我们以历史股价和财经新闻为输入，应用于股票投资组合案例。实证结果表明，在多种场景下均实现收敛，且年化收益优于仅使用优化器的基线方法，验证了两类代理间的协同效应。SOLID为跨领域自动化与智能决策提供了有前景的解决方案。
原文摘要 · Abstract (English)
This paper introduces SOLID (Synergizing Optimization and Large Language Models for Intelligent Decision-Making), a novel framework that integrates mathematical optimization with the contextual capabilities of large language models (LLMs). SOLID facilitates iterative collaboration between optimization and LLMs agents through dual prices and deviation penalties. This interaction improves the quality of the decisions while maintaining modularity and data privacy. The framework retains theoretical convergence guarantees under convexity assumptions, providing insight into the design of LLMs prompt. To evaluate SOLID, we applied it to a stock portfolio investment case with historical prices and financial news as inputs. Empirical results demonstrate convergence under various scenarios and indicate improved annualized returns compared to a baseline optimizer-only method, validating the synergy of the two agents. SOLID offers a promising framework for advancing automated and intelligent decision-making across diverse domains.
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。