LLM Cascade with Multi-Objective Optimal Consideration
<|reference_start|>LLM Cascade with Multi-Objective Optimal Consideration: Large Language Models (LLMs) have demonstrated exceptional capabilities in understanding and generating natural language. However, their high deployment costs often pose a barrier to practical applications, especially. Cascading local and server models offers a promising solution to this challenge. While existing studies on LLM cascades have primarily focused on the performance-cost trade-off, real-world scenarios often involve more complex requirements. This paper introduces a novel LLM Cascade strategy with Multi-Objective Optimization, enabling LLM cascades to consider additional objectives (e.g., privacy) and better align with the specific demands of real-world applications while maintaining their original cascading abilities. Extensive experiments on three benchmarks validate the effectiveness and superiority of our approach.<|reference_end|>
@article{zhang2024llm,
    title={LLM Cascade with Multi-Objective Optimal Consideration},
    author={Kai Zhang, Liqian Peng, Congchao Wang, Alec Go, Xiaozhong Liu},
    journal={arXiv preprint arXiv:2410.08014},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.08014},
    primaryClass={cs.CL}
}