SOLID: a Framework of Synergizing Optimization and LLMs for Intelligent Decision-Making

TL;DR

SOLID is a new framework that combines optimization techniques with large language models to enhance decision-making, demonstrated through improved stock investment returns and theoretical guarantees.

Contribution

It introduces a modular framework integrating optimization and LLMs with convergence guarantees, enabling iterative decision refinement while preserving data privacy.

Findings

Demonstrates convergence in stock investment scenarios

Achieves higher annualized returns than baseline methods

Validates the synergy between optimization and LLMs

Abstract

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.