5 papers
Private-RAG: Answering Multiple Queries with LLMs while Keeping Your Data Private
Ruihan Wu, Erchi Wang, Zhiyuan Zhang +1
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by retrieving documents from an external corpus at inference time. When this corpus contains sensitive in…
Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
Ruihan Wu, Yu-Xiang Wang
Hu and Mehta (2024) posed an open problem: what is the optimal instance-dependent rate for the stochastic decision-theoretic online learning (with actions and rounds) under…
On Speeding Up Language Model Evaluation
Jin Peng Zhou, Christian K. Belardi, Ruihan Wu +4
Developing prompt-based methods with Large Language Models (LLMs) requires making numerous decisions, which give rise to a combinatorial search problem over hyper-parameters. This…
Large Scale Knowledge Washing
Yu Wang, Ruihan Wu, Zexue He +2
Large language models show impressive abilities in memorizing world knowledge, which leads to concerns regarding memorization of private information, toxic or sensitive knowledge,…
Online Feature Updates Improve Online (Generalized) Label Shift Adaptation
Ruihan Wu, Siddhartha Datta, Yi Su +3
This paper addresses the prevalent issue of label shift in an online setting with missing labels, where data distributions change over time and obtaining timely labels is challengi…