5 papers · 1 filter
PETS: A Principled Framework Towards Optimal Trajectory Allocation for Efficient Test-Time Self-Consistency
Zhangyi Liu, Huaizhi Qu, Xiaowei Yin +4
Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories. However, achieving sample-efficient test-time self-consistency under a limited bud…
DOGe: Defensive Output Generation for LLM Protection Against Knowledge Distillation
Pingzhi Li, Zhen Tan, Mohan Zhang +3
Large Language Models (LLMs) represent substantial intellectual and economic investments, yet their effectiveness can inadvertently facilitate model imitation via knowledge distill…
Efficient MAP Estimation of LLM Judgment Performance with Prior Transfer
Huaizhi Qu, Inyoung Choi, Zhen Tan +6
LLM ensembles are widely used for LLM judges. However, how to estimate their accuracy, especially in an efficient way, is unknown. In this paper, we present a principled maximum a…
LucidAtlas: Learning Uncertainty-Aware, Covariate-Disentangled, Individualized Atlas Representations
Yining Jiao, Sreekalyani Bhamidi, Huaizhi Qu +11
The goal of this work is to develop principled techniques to extract information from high dimensional data sets with complex dependencies in areas such as medicine that can provid…
Harnessing Your DRAM and SSD for Sustainable and Accessible LLM Inference with Mixed-Precision and Multi-level Caching
Jie Peng, Zhang Cao, Huaizhi Qu +5
Although Large Language Models (LLMs) have demonstrated remarkable capabilities, their massive parameter counts and associated extensive computing make LLMs' deployment the main pa…