LLMs Generate Robust Portfolios of Optimization Models
A new algorithm uses large language models to produce multiple optimization models, enhancing reliability. The method treats the LLM as both a stochastic generator and a reasoning evaluator, creating a portfolio robust to individual model failures. This addresses the risk of relying on a single LLM-generated model for structured decision-making in resource allocation and planning.
Key facts
- Mathematical optimization is used for structured decision-making in resource allocation and planning.
- Formulating accurate optimization models requires domain and optimization expertise.
- LLMs can generate optimization models from natural language descriptions.
- Single LLM-generated models may be unreliable.
- The proposed algorithm generates a portfolio of optimization models.
- The portfolio is designed to be robust to LLM limitations.
- The LLM plays two roles: stochastic generator and reasoning evaluator.
- The work is published on arXiv with ID 2605.27013.
Entities
Institutions
- arXiv