Problem
Research questions and friction points this paper is trying to address.
black-box stability
input diversity
prior information
unstructured data
output stabilization
Stabilizing black-box algorithms through task-oriented randomization
🤖 AI Summary
This work addresses the challenge that black-box algorithms often struggle to simultaneously ensure stability and effectively leverage prior information when handling inputs with diverse or unstructured forms. To tackle this issue, the authors propose a task-oriented randomization framework that adaptively adjusts its strategy based on the generative mechanism of the input data, thereby enhancing algorithmic stability in complex scenarios. Theoretical analysis provides formal stability guarantees, uncovers an inherent trade-off between stability and exploration, and extends the approach to top-k ranking tasks in large language models. Extensive numerical experiments and evaluations on real-world datasets demonstrate that the proposed method significantly improves both the stability and practical utility of black-box algorithms.
📝 Abstract
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.
Innovation
Methods, ideas, or system contributions that make the work stand out.
task-oriented randomization
black-box stability
generative mechanisms
stability-exploration trade-off
top-k ranking
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