2 citations · 5 across the 22 of their papers we have counts for
7 papers · 1 filter
OLMo-Detect: A Multi-Stage, Confounder-Controlled Benchmark for Membership Inference on Large Language Models
Tao Shi, Chaoyi Xiang, Qiongkai Xu +1
Membership inference on large language models (LLMs) aims to determine whether a given text sample was included in an LLM's training data, without access to its training corpus. De…
Adaptive Cost-Efficient Evaluation for Reliable Patent Claim Generation
Yongmin Yoo, Qiongkai Xu, Longbing Cao
Automated patent claim validation demands low error tolerance. However, existing approaches face a rigidity-resource dilemma: lightweight encoders cannot track long-range legal dep…
Heterogeneous Dependency Graph-Guided Attentionfor Patent Representation Learning
Yongmin Yoo, Qiongkai Xu, Zhangkai Wu +1
Pre-trained language models advance patent classification and retrieval by encoding claims as flat token sequences, but they overlook the dependency hierarchy among claims. Incorpo…
Beyond Theoretical Bounds: Empirical Privacy Loss Calibration for Text Rewriting Under Local Differential Privacy
Weijun Li, Arnaud Grivet Sébert, Qiongkai Xu +2
The growing use of large language models has increased interest in sharing textual data in a privacy-preserving manner. One prominent line of work addresses this challenge through…
Who Wrote the Book? Detecting and Attributing LLM Ghostwriters
Anudeex Shetty, Qiongkai Xu, Olga Ohrimenko +1
In this paper, we introduce GhostWriteBench, a dataset for LLM authorship attribution. It comprises long-form texts (50K+ words per book) generated by frontier LLMs, and is designe…
Fault-Tolerant Evaluation for Sample-Efficient Model Performance Estimators
Zihan Zhu, Yanqiu Wu, Qiongkai Xu
In the era of Model-as-a-Service, organizations increasingly rely on third-party AI models for rapid deployment. However, the dynamic nature of emerging AI applications, the contin…
Semantic Leakage from Image Embeddings
Yiyi Chen, Qiongkai Xu, Desmond Elliott +2
Image embeddings are generally assumed to pose limited privacy risk. We challenge this assumption by formalizing semantic leakage as the ability to recover semantic structures from…