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arXiv · 2312.16119

A bi-objective $\epsilon$-constrained framework for quality-cost optimization in language model ensembles

Aditi Singla·Aditya Singh·Kanishk Kukreja
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Abstract

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.

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Aditi Singla, Aditya Singh, Kanishk Kukreja. 2023-12-26. A bi-objective $\epsilon$-constrained framework for quality-cost optimization in language model ensembles. https://arxiv.org/abs/2312.16119

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