1 citations · 1 across the 7 of their papers we have counts for
7 papers
Capable yet Parsimonious: Extracting and Characterizing Hidden Chain-of-Thought in Frontier Models
Xiaoyu Luo, Tao Ren, Wenrui Yu +3
The rapid capability gains of frontier language models are widely attributed to improved reasoning abilities, yet this cannot be verified as raw CoT traces in closed-source systems…
Characterizing Memorization in Diffusion Language Models: Generalized Extraction and Sampling Effects
Xiaoyu Luo, Wenrui Yu, Qiongxiu Li +1
Autoregressive language models (ARMs) have been shown to memorize and occasionally reproduce training data verbatim, raising concerns about privacy and copyright liability. Diffusi…
APEX: Probing Neural Networks via Activation Perturbation
Tao Ren, Xiaoyu Luo, Qiongxiu Li
Prior work on probing neural networks primarily relies on input-space analysis or parameter perturbation, both of which face fundamental limitations in accessing structural informa…
Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework
Xiaoyu Luo, Yiyi Chen, Qiongxiu Li +1
Large Language Models (LLMs) have been reported to "leak" Personally Identifiable Information (PII), with successful PII reconstruction often interpreted as evidence of memorizatio…
Shared Path: Unraveling Memorization in Multilingual LLMs through Language Similarities
Xiaoyu Luo, Yiyi Chen, Johannes Bjerva +1
We present the first comprehensive study of Memorization in Multilingual Large Language Models (MLLMs), analyzing 95 languages using models across diverse model scales, architectur…
Trustworthy Machine Learning via Memorization and the Granular Long-Tail: A Survey on Interactions, Tradeoffs, and Beyond
Qiongxiu Li, Xiaoyu Luo, Yiyi Chen +1
The role of memorization in machine learning (ML) has garnered significant attention, particularly as modern models are empirically observed to memorize fragments of training data.…
DeMem: Privacy-Enhanced Robust Adversarial Learning via De-Memorization
Xiaoyu Luo, Qiongxiu Li
Adversarial robustness, the ability of a model to withstand manipulated inputs that cause errors, is essential for ensuring the trustworthiness of machine learning models in real-w…