We propose an ensembling framework that uses diverse open-sourced Large Language Models (LLMs) to achieve high response quality while maintaining cost efficiency. We formulate a bi-objective optimization problem to represent the quality-cost tradeoff and then introduce an additional budget constraint that reduces the problem to a straightforward 0/1 knapsack problem. We empirically demonstrate that our framework outperforms the existing ensembling approaches in response quality while significantly reducing costs.

翻译：我们提出了一种集成框架，利用多样化的开源大语言模型（LLMs），在保持成本效益的同时实现高响应质量。我们构建了一个双目标优化问题以表示质量与成本的权衡，随后引入额外的预算约束，将问题简化为直接的0/1背包问题。实验结果表明，我们的框架在响应质量上优于现有集成方法，同时显著降低了成本。