LLM Cascade with Multi-Objective Optimal Consideration

ICLR 2025 Conference Submission1974 Authors

Keywords: LLM Cascade; Multi-Objective; On-device Intelligence

TL;DR: This paper studies a LLM cascade system with Multi-objective considerations (e.g., privacy). The experiments verify the necessity and effectiveness of proposed methods.

Abstract: 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.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 1974