ModelEquivBench: Certifying Multi-Relational Evaluation of LLM-Generated Optimization Models· ModelEquivBench：LLM 生成优化模型的多关系评估
Large language models increasingly generate optimization models from natural language, but existing evaluation often reduces a generated model and its ground truth to a single equivalent/not-equivalent verdict or an execution-success rate--labels that are neither independently checkable nor faithful to the multiple distinct senses in which two formulations can agree. We present ModelEquivBench, a certifying, multi-relational evaluation system that reports a per-pair semantic profile E0--E6: model construction and exact ingestion (E0), verified representation alignment (E1), same-space and projected feasible-set relations (E2, E3), objective-order equivalence (E4), optimal-value equality (E5), and optimizer-set equivalence (E6). Each decided entry carries relation-appropriate, independently
模型生成优化评估新方法，超越传统二元评价。
- 核心方法
- 提出 ModelEquivBench 系统，通过多个语义层面 E0-E6 评估 LLM 生成的优化模型。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 现有评估方法无法准确反映大型语言模型生成的优化模型与真实模型之间的复杂关系。
- 关键实验
- 未提供
- 主要贡献
- 提供多关系评估，使模型评估更加全面和准确。
- 意义与局限
- 促进优化模型生成技术的发展，提升模型评估的透明度和可靠性；局限在于具体应用场景的适用性和评估效率。