通过任务导向随机化提升黑箱模型稳定性。
Stabilizing black-box algorithms through task-oriented randomization
- 根据输入数据生成机制自适应调整随机策略。
- 理论证明了稳定性与探索性的内在权衡关系。
- 适用于大模型排序等实际场景，效果经实证验证。
随着黑箱模型在现代研究中日益成为基础，确保其稳定性对实现可信赖的人工智能至关重要。输入数据的固有多样性——从结构化的高斯分布到未知结构的复杂数据——带来了重大挑战：如何在有效利用先验信息的同时稳定黑箱输出。本文提出一种任务导向的随机化方法，能够根据输入数据的潜在生成机制自适应调整策略，特别针对无结构复杂性问题。研究提出了全面的稳定性保障体系，不仅建立了稳定性坚实的理论基础，还深入分析了稳定性与探索性之间的内在权衡。受大型语言模型架构启发，该框架进一步扩展至top-k排名问题。通过大量数值模拟及真实数据集应用，验证了该方法的有效性与可行性。
原文摘要 · Abstract (English)
As black-box models become foundational to modern research, ensuring their stability is paramount for the realization of trustworthy artificial intelligence. The inherent diversity of inputs - ranging from structured Gaussian distributions to complex data with unknown structures - poses a significant challenge: how to stabilize black-box outputs while effectively leveraging available prior information. This paper introduces a task-oriented randomization methodology that adaptively tailors its strategy to the underlying generative mechanisms of the input data, specifically addressing unstructured complexities. A comprehensive suite of stability guarantees is proposed. Beyond establishing rigorous theoretical foundations for stability, the research provides a detailed analysis of the intrinsic trade-off between stability and exploration. Motivated by the architecture of Large Language Models, the framework is further extended to top-k ranking problems. The validity and effectiveness of the proposal are demonstrated through extensive numerical simulations and applications to the real-world dataset.
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