2 citations · 5 across the 22 of their papers we have counts for
12 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…
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…
PatentScore: Multi-dimensional Evaluation of LLM-Generated Patent Claims
Yongmin Yoo, Qiongkai Xu, Longbing Cao
High-stakes texts such as patent claims, medical records, and technical reports are structurally complex and demand a high degree of reliability and precision. While large language…
Overview of the 2024 ALTA Shared Task: Detect Automatic AI-Generated Sentences for Human-AI Hybrid Articles
Diego Mollá, Qiongkai Xu, Zijie Zeng +1
The ALTA shared tasks have been running annually since 2010. In 2024, the purpose of the task is to detect machine-generated text in a hybrid setting where the text may contain por…
Cut the Deadwood Out: Backdoor Purification via Guided Module Substitution
Yao Tong, Weijun Li, Xuanli He +2
Model NLP models are commonly trained (or fine-tuned) on datasets from untrusted platforms like HuggingFace, posing significant risks of data poisoning attacks. A practical yet und…
IDT: Dual-Task Adversarial Attacks for Privacy Protection
Pedro Faustini, Shakila Mahjabin Tonni, Annabelle McIver +2
Natural language processing (NLP) models may leak private information in different ways, including membership inference, reconstruction or attribute inference attacks. Sensitive in…
SEEP: Training Dynamics Grounds Latent Representation Search for Mitigating Backdoor Poisoning Attacks
Xuanli He, Qiongkai Xu, Jun Wang +2
Modern NLP models are often trained on public datasets drawn from diverse sources, rendering them vulnerable to data poisoning attacks. These attacks can manipulate the model's beh…
Backdoor Attack on Multilingual Machine Translation
Jun Wang, Qiongkai Xu, Xuanli He +2
While multilingual machine translation (MNMT) systems hold substantial promise, they also have security vulnerabilities. Our research highlights that MNMT systems can be susceptibl…
TuBA: Cross-Lingual Transferability of Backdoor Attacks in LLMs with Instruction Tuning
Xuanli He, Jun Wang, Qiongkai Xu +4
The implications of backdoor attacks on English-centric large language models (LLMs) have been widely examined - such attacks can be achieved by embedding malicious behaviors durin…
Boot and Switch: Alternating Distillation for Zero-Shot Dense Retrieval
Fan Jiang, Qiongkai Xu, Tom Drummond +1
Neural 'dense' retrieval models are state of the art for many datasets, however these models often exhibit limited domain transfer ability. Existing approaches to adaptation are un…